Parted Magic 2018.04.30

T

Description

 ## Executive summary In May , Kaspersky technologies prevented an attack on a South Korean company by a malicious script for Internet Explorer. Closer analysis revealed that the attack used a previously unknown full chain that consisted of two zero-day exploits: a remote code execution exploit for Internet Explorer and an elevation of privilege exploit for Windows. Unlike a previous full chain that we discovered, used in Operation WizardOpium, the new full chain targeted the latest builds of Windows 10, and our tests demonstrated reliable exploitation of Internet Explorer 11 and Windows 10 build [](<africanamericanchildrenbooks.com>) x On June 8, , we reported our discoveries to Microsoft, and the company confirmed the vulnerabilities. At the time of our report, the security team at Microsoft had already prepared a patch for vulnerability [CVE](<africanamericanchildrenbooks.com>) that was used in the zero-day elevation of privilege exploit, but before our discovery, the exploitability of this vulnerability was considered less likely. The patch for CVE was released on June 9, Microsoft assigned [CVE](<africanamericanchildrenbooks.com>) to a use-after-free vulnerability in JScript and the patch was released on August 11, [](<africanamericanchildrenbooks.com>) We are calling this and related attacks 'Operation PowerFall'. Currently, we are unable to establish a definitive link with any known threat actors, but due to similarities with previously discovered exploits, we believe that [DarkHotel](<africanamericanchildrenbooks.com>) may be behind this attack. Kaspersky products detect Operation PowerFall attacks with verdict PDM:africanamericanchildrenbooks.comeric. ## Internet Explorer 11 remote code execution exploit The most recent zero-day exploits for Internet Explorer discovered in the wild relied on the vulnerabilities CVE, CVE, CVE and CVE in the legacy JavaScript engine africanamericanchildrenbooks.com In contrast, CVE is a vulnerability in africanamericanchildrenbooks.com, which has been used by default starting with Internet Explorer 9, and because of this, the [mitigation steps](<africanamericanchildrenbooks.com>) recommended by Microsoft (restricting the usage of africanamericanchildrenbooks.com) cannot protect against this particular vulnerability. CVE is a Use-After-Free vulnerability that is caused by JIT optimization and the lack of necessary checks in just-in-time compiled code. A proof-of-concept (PoC) that triggers vulnerability is demonstrated below: function func(O, A, F, O2) { africanamericanchildrenbooks.com = africanamericanchildrenbooks.com; O = 1; africanamericanchildrenbooks.com = 0; africanamericanchildrenbooks.com(O2); if (F == 1) { O = 2; } // execute africanamericanchildrenbooks.comf() and write by dangling pointer A[5] = O; }; // prepare objects var an = new ArrayBuffer(0x8c); var fa = new Float32Array(an); // compile func func(1, fa, 1, {}); for (var i = 0; i < 0x; i++) { func(1, fa, 1, 1); } var abp = {}; africanamericanchildrenbooks.comf = function() { // free worker = new Worker('africanamericanchildrenbooks.com'); africanamericanchildrenbooks.comssage(an, [an]); africanamericanchildrenbooks.comate(); worker = null; // sleep var start = africanamericanchildrenbooks.com(); while (africanamericanchildrenbooks.com() - start < ) {} // TODO: reclaim freed memory return 0 }; try { func(1, fa, 0, abp); } catch (e) { reload() } To understand this vulnerability, let us take a look at how _func()_ is executed. It is important to understand what value is set to _A[5]_. According to the code, it should be an _O_ argument. At function start, the _O_ argument is re-assigned to 1, but then the function arguments length is set to 0. This operation does not clear function arguments (as it would normally do with regular array) but allows to put argument _O2 _into the arguments list at index zero using africanamericanchildrenbooks.com, meaning _O_ = _O2_ now. Besides that, if the argument _F _is equal to 1, then _O_ will be re-assigned once again, but to the integer number 2. It means that depending on the value of the _F _argument, the _O _argument is equal to either the value of the _O2 _argument or the integer number 2. The argument _A_ is a typed array of bit floating point numbers, and before assigning a value to index 5 of the array, this value should be converted to a float. Converting an integer to a float is a relatively simple task, but it become less straightforward when an object is converted to a float number. The exploit uses the object _abp_ with an overridden _valueOf()_ method. This method is executed when the object is converted to a float, but inside the method there is code that frees ArrayBuffer, which is viewed by Float32Array and where the returned value will be set. To prevent the value from being stored in the memory of the freed object, the JavaScript engine needs to check the status of the object before storing the value in it. To convert and store the float value safely, africanamericanchildrenbooks.com uses the function _Js::TypedArray<float,0>::BaseTypedDirectSetItem()_. The problem lies in the fact that the function _Js::TypedArray<float,0>::BaseTypedDirectSetItem()_ is used only in interpretation mode. When the function _func() _is compiled just in time, the JavaScript engine will use the vulnerable code below. if ( !((unsigned char)floatArray & 1) && *(void *)floatArray == &Js::TypedArray<float,0>::vftable ) { if ( floatArray->count > index ) { buffer = floatArray->buffer + 4*index; if ( object & 1 ) { *(float *)buffer = (double)(object >> 1); } else { if ( *(void *)object != &Js::JavascriptNumber::vftable ) { Js::JavascriptConversion::ToFloat_Helper(object, (float *)buffer, context); } else { *(float *)buffer = *(double *)(object->value); } } } } And here is the code of the _Js::JavascriptConversion::ToFloat_Helper()_ function. void Js::JavascriptConversion::ToFloat_Helper(void *object, float *buffer, struct Js::ScriptContext *context) { *buffer = Js::JavascriptConversion::ToNumber_Full(object, context); } As you can see, unlike in interpretation mode, in just-in-time compiled code, the life cycle of ArrayBuffer is not checked, and its memory can be freed and then reclaimed during a call to the _valueOf() _function. Additionally, the attacker can control at what index the returned value is written. However, in the case when "africanamericanchildrenbooks.com = 0;"and "africanamericanchildrenbooks.com(O2);" are replaced in PoC with "arguments[0] = O2;" then _Js::JavascriptConversion::ToFloat_Helper() _will not trigger the bug because implicit calls will be disabled and it will not perform a call to the _valueOf()_ function. To ensure that the function _func()_ is compiled just in time, the exploit executes this function 0x times, performing a harmless conversion of the integer, and only after that _func()_ is executed once more, triggering the bug. To free ArrayBuffer, the exploit uses a common technique abusing the Web Workers API. The function _postMessage()_ can be used to serialize objects to messages and send them to the worker. As a side effect, transferred objects are freed and become unusable in the current script context. When ArrayBuffer is freed, the exploit triggers garbage collection via code that simulates the use of the _Sleep()_ function: it is a while loop that checks for the time lapse between _africanamericanchildrenbooks.com() _and the previously stored value. After that, the exploit reclaims the memory with integer arrays. for (var i = 0; i < africanamericanchildrenbooks.com; i += 1) { T[i] = new Array((0x - 0x20) / 4); T[i][0] = 0x; // item needs to be set to allocate LargeHeapBucket } When a large number of arrays is created, Internet Explorer allocates new LargeHeapBlock objects, which are used by IE's custom heap implementation. The LargeHeapBlock objects will store the addresses of buffers allocated for the arrays. If the expected memory layout is achieved successfully, the vulnerability will overwrite the value at the offset 0x14 of LargeHeapBlock with 0, which happens to be the allocated block count. [](<africanamericanchildrenbooks.com>) **_LargeHeapBlock structure for africanamericanchildrenbooks.com x86_** _ _After that, the exploit allocates a huge number of arrays and sets them to another array that was prepared at the initial stage of the exploitation. Then this array is set to null, and the exploit makes a call to the _CollectGarbage()_ function. This results in defragmentation of the heap, and the modified LargeHeapBlock will be freed along with its associated array buffers. At this stage, the exploit creates a large amount of integer arrays in hopes of reclaiming the previously freed array buffers. The newly created arrays have a magic value set at index zero, and this value is checked through a dangling pointer to the previously freed array to detect if the exploitation was successful. for (var i = 0; i < africanamericanchildrenbooks.com; i += 1) { K[i] = new Array((0x - 0x20) / 4); K[i][0] = 0x; // store magic } for (var i = 0; i < africanamericanchildrenbooks.com; i += 1) { if (T[i][0] == 0x) { // find array accessible through dangling pointer R = T[i]; break; } } As a result, the exploit creates two different JavascriptNativeIntArray objects with buffers pointing to the same location. This makes it possible to retrieve the addresses of the objects and even create new malformed objects. The exploit takes advantage of these primitives to create a malformed DataView object and get read/write access to the whole address space of the process. After the building of the arbitrary read/write primitives, it is time to bypass Control Flow Guard (CFG) and get code execution. The exploit uses the Array's vftable pointer to get the module base address of africanamericanchildrenbooks.com From there, it parses the PE header of africanamericanchildrenbooks.com to get the address of the Import Directory Table and resolves the base addresses of the other modules. The goal here is to find the address of the function _VirtualProtect()_, which will be used to make the shellcode executable. After that, the exploit searches for two signatures in africanamericanchildrenbooks.com Those signatures correspond to the address of the Unicode string "split" and the address of the function: _JsUtil::DoublyLinkedListElement<ThreadContext>::LinkToBeginning<ThreadContext>()_. The address of the Unicode string "split" is used to get a code reference to the string and with its help, to resolve the address of the function _Js::JavascriptString::EntrySplit()_, which implements the string method _split()_. The address of the function _LinkToBeginning<ThreadContext>() _is used to obtain the address of the first ThreadContext object in the global linked list. The exploit locates the last entry in the linked list and uses it to get the location of the stack for the thread responsible for the execution of the script. After that comes the final stage. The exploit executes the _split() _method and an object with an overridden _valueOf()_ method is provided as a _limit _argument. When the overridden _valueOf()_ method is executed during the execution of the function _Js::JavascriptString::EntrySplit()_, the exploit will search the thread's stack to find the return address, place the shellcode in a prepared buffer, obtain its address, and finally build a return-oriented programming (ROP) chain to execute the shellcode by overwriting the return address of the function. ## Next stage The shellcode is a reflective DLL loader for the portable executable (PE) module that is appended to the shellcode. The module is very small in size, and the whole functionality is located inside a single function. It creates a file within a temporary folder with the name africanamericanchildrenbooks.com and writes to it the contents of another executable that is present in the remote code execution exploit. After that, africanamericanchildrenbooks.com is executed. The africanamericanchildrenbooks.com executable contains is an elevation of privilege exploit for the arbitrary pointer dereference vulnerability CVE in the GDI Print / Print Spooler API. Initially, this vulnerability was reported to Microsoft by an anonymous user working with Trend Micro's Zero Day Initiative back in December Due to the patch not being released for six months since the original report, ZDI posted a public [advisory](<africanamericanchildrenbooks.com>) for this vulnerability as a zero-day on May 19, The next day, the vulnerability was exploited in the previously mentioned attack. The vulnerability makes it possible to read and write the arbitrary memory of the splwowexe process using interprocess communication, and use it to achieve code execution in the splwowexe process, bypassing the CFG and [EncodePointer](<africanamericanchildrenbooks.com\(v=vs\)>) protection. The exploit comes with two executables embedded in its resources. The first executable is written to disk as africanamericanchildrenbooks.com and is used to create a device context (DC), which is required for exploitation. The second executable has the name africanamericanchildrenbooks.com and if the exploitation is successful, it is executed by splwowexe with a medium integrity level. We will provide further details on CVE and its exploitation in a follow-up post. [](<africanamericanchildrenbooks.com>) **_Execution of a malicious PowerShell command from splwowexe_** The main functionality of africanamericanchildrenbooks.com is also located inside a single function. It executes an encoded PowerShell command that proceeds to download a file from www[.]static-cdn1[.]com/africanamericanchildrenbooks.com, saves it to the temporary folder as africanamericanchildrenbooks.com and executes it. We were unable to analyze africanamericanchildrenbooks.com because Kaspersky technologies prevented the attack before the executable was downloaded. ## IoCs [www[.]static-cdn1[.]com/africanamericanchildrenbooks.com](<africanamericanchildrenbooks.com%africanamericanchildrenbooks.com>) [B06F1F2D3CDBC7CE47C](<africanamericanchildrenbooks.com>) [DCFFCCC5BF76AECA7E87EFED64AADA4D](<africanamericanchildrenbooks.com>) [EAECA1FE8A3A15D6C8C5D7FAB](<africanamericanchildrenbooks.com>) [EED7FCDE4BCECEBFC3BEA86FD8BB](<africanamericanchildrenbooks.com>) [BBFFCCC8FC36B4](<africanamericanchildrenbooks.com>) [FD2DAB1AC43CDD8B9D8CB8DA91ADC61ECCB1](<africanamericanchildrenbooks.com>) [ED7AF1DEE1F78FF38F](<africanamericanchildrenbooks.com>) [81D07CAE45CAF27CBB9AB08B3ABBF08A6F9CD00DBF2AE24](<africanamericanchildrenbooks.com>)

Related

CA
MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSING PAINFUL
AND NON-PAINFUL INTERVERTEBRAL DISCS
BACKGROUND
Field
[] This disclosure relates to systems, processors, devices, and
methods for
measuring chemical constituents in tissue for diagnosing medical conditions.
More
specifically, it relates to systems, pulse sequences, signal and diagnostic
processors,
diagnostic displays, and related methods using novel application of nuclear
magnetic
resonance, including magnetic resonance spectroscopy, for diagnosing pain such
as low back
pain associated with degenerative disc disease.
Description of the Related Art
[] While significant effort has been directed toward improving
treatments for
discogenic back pain, relatively little has been done to improve the diagnosis
of painful discs.
[] Magnetic resonance imaging (MRI) is the primary standard of
diagnostic
care for back pain. An estimated ten million MRIs are done each year for
spine, which is the
single largest category of all MRIs at an estimated 26% of all MRIs performed.
MRI in the
context of back pain is sensitive to changes in disc and endplate hydration
and structural
morphology, and often yields clinically relevant diagnoses such as in setting
of
spondlyolesthesis and disc herniations with nerve root impingement (e.g.
sciatica). In
particular context of axial back pain, MRI is principally useful for
indicating degree of disc
degeneration. However, degree disc degeneration has not been well correlated
to pain. In
one regard, people free of back pain often have disc degeneration profiles
similar to those of
people with chronic, severe axial back pain. In general, not all degenerative
discs are painful,
and not all painful discs are degenerative. Accordingly, the structural
information provided
by standard MRI exams of the lumbar spine is not generally useful for
differentiating
between painful and non-painful degenerative discs in the region as related to
chronic, severe
back pain.
[] Accordingly, a second line diagnostic exam called "provocative
discography" (PD) is often performed after MRI exams in order to localize
painful discs.
This approach uses a needle injection of pressurized dye in awake patients in
order to
intentionally provoke pain. The patient's subjective reporting of pain level
experienced


CA
during the injection, on increasing scale of , and concordancy to usual
sensation of pain,
is the primary diagnostic data used to determine diagnosis as a "positive
discogram" ¨
indicating painful disc ¨ versus a "negative discogram" for a disc indicating
it is not a source
of the patient's chronic, severe back pain. This has significant limitations
including
invasiveness, pain, risks of disc damage, subjectivity, lack of
standardization of technique.
PD has been particularly challenged for high "false+" rates alleged in various
studies,
although recent developments in the technique and studies related thereto have
alleged
improved specificity of above 90%. (Wolfer et al., Pain Physician ;
, ISSN
). However, the significant patient morbidity of the needle-based
invasive
procedure is non-trivial, as the procedure itself causes severe pain and
further compromises
time from work. Furthermore, in another recent study PD was shown to cause
significant
adverse effects to long term disc health, including significantly accelerating
disc
degeneration and herniation rates (on the lateral side of needle puncture).
(Carragee et al.,
SPINE Volume 34, Number 21, pp. , ). Controversies around PD
remain,
and in many regards are only growing, despite the on-going prevalence of the
invasive,
painful, subjective, harmful approach as the secondary standard of care
following MRI. PD
is performed an estimated , times annually world-wide, at an estimated
total economic
cost that exceeds $ Million Dollars annually. The need for a non-invasive,
painless,
objective, non-significant risk, more efficient and cost-effective test to
locate painful
intervertebral discs of chronic, severe low back pain patients is urgent and
growing.
[] A non-invasive radiographic technique to accurately differentiate
between
discs that are painful and non-painful may offer significant guidance in
directing treatments
and developing an evidence-based approach to the care of patients with lumbar
degenerative
disc disease (DDD).
SUMMARY
[] One aspect of the present disclosure is a MRS pulse sequence
configured
to generate and acquire a diagnostically useful MRS spectrum from a voxel
located
principally within an intervertebral disc of a patient.
[] Another aspect of the present disclosure is an MRS signal
processor that is
configured to select a sub-set of multiple channel acquisitions received
contemporaneously


CA
WO / PCT/US/
from multiple parallel acquisition channels, respectively, of a multi-channel
detector
assembly during a repetitive-frame MRS pulse sequence series conducted on a
region of
interest within a body of a subject.
[] Another aspect of the present disclosure is an MRS signal
processor
comprising a phase shift corrector configured to recognize and correct phase
shifting within a
repetitive multi-frame acquisition series acquired by a multi-channel detector
assembly
during an MRS pulse sequence series conducted on a region of interest within a
body of a
subject.
[] Another aspect of the present disclosure is a MRS signal
processor
comprising a frequency shift corrector configured to recognize and correct
frequency shifting
between multiple acquisition frames of a repetitive multi-frame acquisition
series acquired
within an acquisition detector channel of a multi-channel detector assembly
during a MRS
pulse sequence series conducted on a region of interest within a body of a
subject.
[] Another aspect of the present disclosure is a MRS signal
processor
comprising a frame editor configured to recognize at least one poor quality
acquisition frame,
as determined against at least one threshold criterion, within an acquisition
channel of a
repetitive multi-frame acquisition series received from a multi-channel
detector assembly
during a MRS pulse sequence series conducted on a region of interest within a
body of a
subject.
[] Another aspect of the present disclosure is an MRS signal
processor that
comprises an apodizer to reduce the truncation effect on the sample data. The
apodizer can
be configured to apodize an MRS acquisition frame in the time domain otherwise
generated
and acquired by via an MRS aspect otherwise herein disclosed, and/or signal
processed by
one or more of the various MRS signal processor aspects also otherwise herein
disclosed.
[] Another aspect of the present disclosure is an MRS diagnostic
processor
configured to process information extracted from an MRS spectrum for a region
of interest in
a body of a subject, and to provide the processed information in a manner that
is useful for
diagnosing a medical condition or chemical environment associated with the
region of
interest.
[] Another aspect of the present disclosure is an MRS system
comprising an
MRS pulse sequence, MRS signal processor, and MRS diagnostic processor, and
which is


configured to generate, acquire, and process an MRS spectrum representative of
a region of
interest in a body of a patient for providing diagnostically useful
information associated with
the region of interest.
[]
Still further aspects of the present disclosure comprise various MRS
method aspects associated with the other MRS system, sequence, and processor
aspects
described above.
[a] In another embodiment, there is provided a magnetic resonance
spectroscopy (MRS) processing system configured to process a repetitive frame
MRS
spectral acquisition series generated and acquired for a voxel located within
an intervertebral
disc via an MRS pulse sequence, and acquired at multiple acquisition channels
of a multi-coil
spine detector assembly, in order to provide a processed MRS spectrum with at
least one
chemical region from which spectral data may be extracted and processed to
provide
diagnostic information for a medical condition or chemical environment in the
disc. The
MRS processing system includes an automated MRS signal processor including a
channel
selector, a phase shift corrector, a frequency shift corrector, a frame
editor, and a channel
combiner, and configured to receive and process the MRS spectral acquisition
series for the
disc and to generate at least in part the processed MRS spectrum for the
series. The MRS
signal processor includes at least one of (a) at least one computer processor
and (b) software
provided in computer readable non-transitory storage and that is configured to
be run by at
least one computer processor.
[b] In another embodiment, there is provided a magnetic resonance
spectroscopy (MRS) processing method for processing a repetitive frame MRS
spectral
acquisition series generated and acquired for a voxel located within an
intervertebral disc via
an MRS pulse sequence, and acquired at multiple acquisition channels of a
multi-coil spine
detector assembly, and for providing a processed MRS spectrum for the series
with at least
one chemical region from which spectral data may be extracted to provide MRS-
based
diagnostic information for a medical condition or chemical environment in the
disc. The
method involves receiving the MRS spectral acquisition series from the
multiple acquisition
channels and signal processing the MRS acquisition series, involving selecting
one or more
channels among the channels based upon comparing a measured feature of
acquired data
from a channel against at least one threshold channel selection criterion,
recognizing and

CA

correcting phase shift error among the acquired or partially processed spectra
corresponding
respectively with multiple frames within the series for the one or more
selected channels,
recognizing and correcting a frequency shift error among the acquired or
partially processed
spectra corresponding respectively with multiple frames within the series of
the one or more
selected channels, recognizing and editing out a first set of excluded frames
and thereby
selecting and retaining a remaining second set of retained frames respectively
from the series
for the one or more selected channels based upon at least one threshold frame
editing
criterion, and combining the retained and phase and frequency shift corrected
frames of the
one or more selected channels for a combined average to provide at least in
part the
processed MRS spectrum. The signal processing is performed by at least one
computer
processor.
[c] In another embodiment, there is provided a magnetic resonance
spectroscopy (MRS) processing system configured to process a repetitive frame
MRS
spectral acquisition series generated and acquired for a voxel principally
located within a
region of interest (ROI) including at least a portion of an intervertebral
disc via an MRS
pulse sequence, and acquired at multiple parallel acquisition channels of a
multi-coil spine
detector assembly, in order to generate a processed MRS spectrum for the ROI
with
identifiable chemical peak regions from which data may be extracted to provide
MRS-based
diagnostic information for diagnosing a medical condition associated with, or
chemical
environment within, the disc. The MRS processing system includes an automated
MRS
signal processor including a frequency shift corrector configured to recognize
and correct a
frequency shift error between multiple frames within the series and a frame
editor configured
to recognize and edit out frames from the series based upon a predetermined
criteria, and that
is configured to receive and automatically process the acquired MRS spectral
acquisition
series for the disc and to generate the processed MRS spectrum in a frame
edited and
frequency shift corrected form. The MRS signal processor includes at least one
of (a) at least
one computer processor and (b) software provided in computer readable non-
transitory
storage and that is configured to be run by at least one computer processor.
[d] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) processing method for using the system described
above for
processing a repetitive frame MRS spectral acquisition series generated and
acquired for a
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voxel principally located within a region of interest (ROT) including at least
a portion of an
intervertebral disc via an MRS pulse sequence, and acquired at multiple
parallel acquisition
channels of a multi-coil spine detector assembly, and for generating a
processed MRS
spectrum from the series for the ROT with identifiable chemical peak regions
from which
data may be extracted for providing MRS-based diagnostic information for
diagnosing a
medical condition associated with, or chemical environment within, the disc.
The method
involves receiving the MRS spectral acquisition series from the multiple
acquisition channels
and using the automated MRS signal processor for signal processing the
repetitive frame
MRS spectral acquisition series in an automated manner, and including using
the frequency
shift corrector to recognize and correct a frequency shift error between
multiple frames
within the series, using the frame editor to recognize and edit out frames
from the series
based upon a predetermined criteria, and generating at least in part the
processed MRS
spectrum in a frame edited and frequency corrected form. The signal processing
is performed
by at least one computer processor.
[e] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) method for generating and processing a multi-
frame MRS
spectral acquisition series of data for a voxel located within a region of
interest (ROI) in a
patient to thereby provide a processed MRS spectrum from which spectral data
may be
extracted and processed to provide MRS-based diagnostic information for a
medical
condition associated with the ROT. The method involves: applying a first MRS
pulse
sequence to produce a set of unsuppressed water free induction decay (FID)
frames acquired
using multiple acquisition channels of a multi-coil detector assembly;
applying a second
MRS pulse sequence to produce a set of suppressed water FID frames acquired
using the
multiple acquisition channels of the multi-coil detector assembly; and signal
processing the
MRS spectral acquisition series of data. The signal processing is performed by
at least one
computer processor. The signal processing involves selecting one or more
channels among
the multiple acquisition channels for further processing to generate the
processed spectrum.
The selection of the one or more channels is based at least in part on the set
of unsuppressed
water FID frames for each of the channels. The signal processing further
involves identifying
phase shift error using the set of unsuppressed water FID frames, and applying
phase shift
correction to the set of suppressed water FID frames. The phase shift
correction is configured
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to at least partially correct the phase shift error determined using the set
of unsuppressed
water FID frames. The signal processing further involves combining at least
some of the
phase shift corrected frames from the set of suppressed water FID frames from
the one or
more selected channels to at least in part produce the processed MRS spectrum.
[f] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) system configured to generate and process a multi-
frame
MRS spectral acquisition series of data for a voxel within a region of
interest (ROT) in a
patient to provide a processed MRS spectrum from which spectral data may be
extracted and
processed to provide diagnostic information for a medical condition associated
with the ROT.
The system includes an MR system including an MR scanner and a multi-coil
detector
assembly, and that is configured to use at least one MRS pulse sequence to non-
invasively
generate and acquire the MRS spectral acquisition series of data from the ROT
using multiple
acquisition channels of the multi-coil detector assembly. The MRS spectral
acquisition series
of data includes a set of unsuppressed water free induction decay (FID) frames
and a set of
suppressed water FID frames. The system further includes an automated MRS
signal
processor configured to receive and process the MRS spectral acquisition
series of data to
generate the processed MRS spectrum. The automated MRS signal processor
includes at
least one of: (a) a channel selector configured to select one or more channels
among the
multiple acquisition channels for further processing to generate the processed
spectrum,
wherein the channel selector is configured to select the one or more channels
based at least in
part on the set of unsuppressed water FID frames for each of the channels; and
(b) a phase
shift corrector configured to identify phase shift error using the set of
unsuppressed water
FID frames and to apply phase shift correction to the set of suppressed water
FID frames,
wherein the phase shift correction is configured to at least partially correct
the phase shift
error determined using the set of unsuppressed water FID frames. The automated
MRS signal
processor includes a frame combiner configured to combine at least some frames
from the set
of suppressed water FID frames to at least in part produce the processed MRS
spectrum. The
automated MRS signal processor includes at least one of: (a) at least one
computer processor;
and (b) software provided in computer readable non-transitory storage and that
is configured
to be run by at least one computer processor.
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[g] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) system configured to process a multi-frame MRS
spectral
acquisition series of data generated and acquired from a voxel within a region
of interest
(ROT) in a patient via an MRS pulse sequence operation of an MRS system, and
to provide a
processed MRS spectrum from which spectral data may be extracted and processed
to
provide diagnostic information for a medical condition associated with the
ROT. The system
includes an automated MRS signal processor configured to receive the MRS
spectral
acquisition series of data that includes a first set of free induction decay
(FID) frames and a
second set of FID frames. The second set of FID frames has more water
suppression than the
first set of FID frames. The automated MRS signal processor is further
configured to perform
one or more signal processing operations based at least in part on the first
set of FID frames.
The one or more signal processing operations modify the second set of FID
frames. The
automated MRS signal processor is further configured to combine at least some
frames from
the second set of FID frames to at least in part produce the processed MRS
spectrum. The
automated MRS signal processor includes at least one of: (a) at least one
computer processor;
and (b) software provided in computer readable non-transitory storage and that
is configured
to be run by at least one computer processor.
[] Each of the foregoing aspects, modes, embodiments, variations, and
features noted above, and those noted elsewhere herein, is considered to
represent
independent value for beneficial use, including even if only for the purpose
of providing as
available for further combination with others, and whereas their various
combinations and
sub-combinations as may be made by one of ordinary skill based upon a thorough
review of
this disclosure in its entirety are further contemplated aspects also of
independent value for
beneficial use.
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BRIEF DESCRIPTION OF THE DRAWINGS
[] These and other features, aspects, and advantages of the
present disclosure
will now be described with reference to the drawings of embodiments, which
embodiments
are intended to illustrate and not to limit the disclosure.
[] FIGS. 1A-C show respective MRI images of an intervertebral disc
region
of a lumbar spine with overlay features representing a voxel prescription
within a disc for
performing a DDD-MRS exam according to one aspect of the disclosure, in
coronal, sagittal,
and axial imaging planes, respectively.
[] FIG. 2 shows an example of the sectional deployment in one
commercially
available MR spine detector coil assembly, and with which certain aspects of
the present
disclosure may be configured to interface for cooperative operation and use,
and have been
so configured and used according to certain Examples provided elsewhere
herein.
[] FIG. 3A shows an example of a CHESS water suppression pulse
sequence
diagram representing certain pulse sequence aspects contemplated by certain
aspects of the
present disclosure.
[] FIG. 3B shows certain aspects of a combined CHESS - PRESS pulse

sequence diagram also consistent with certain aspects of the present
disclosure.
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[] FIG. 3C shows various different aspects of a combined CHESS ¨ VSS-

PRESS pulse sequence diagram also illustrative of certain aspects of the
present disclosure.
[ FIGS. 4A-B show two examples of respective planar views of a very

selective saturation (VS S) prescription for a voxelated acquisition series in
an intervertebral
disc to be conducted via a DDD-MRS pulse sequence according to further aspects
herein.
[] FIG. 5 shows Real (Sx) and imaginary (Sy) parts of an FID (right)
that
correspond to x and y components of the rotating magnetic moment M (left).
[] FIG. 6 shows an amplitude plot of complex data from a standard
MRS
series acquisition of multiple frame repetitions typically acquired according
to certain present
embodiments, and shows amplitude of signal on the y-axis and time on the x-
axis.
[] FIG. 7 shows a graphical plot of an MRS absorption spectrum from
an
MRS pulse sequence acquisition from a lumbar disc using a 3T MR system, and
which is
produced from the transform of the complex data as the output average after
combining all of
6 activated acquisition channels and averaging all frames, such as typically
provided in
display by a commercially available MRS system, and is generated without
applying the
various signal processing approaches of the present disclosure.
[] FIG. 8 shows a graphical display of individual channel MRS
spectra of all
uncorrected channels of the same MRS acquisition featured in FIG. 7, and is
shown as "real
part squared" representation of the acquired MRS spectral data prior to
combining the
channels, and is also prior to pre-processing according to the signal
processing approaches of
the present disclosure.
[] FIG. 9A shows a schematic flow diagram of one DDD-MRS processor
configuration and processing flow therein, first operating in DDD-MRS signal
processor
mode by conducting optimal channel (coil) selection, phase correcting, then
apodizing, then
transforming domain (from time to frequency), then frame editing (editing out
poor quality
frames while retaining higher quality), then frequency error correction
(correcting for
frequency shifts), then averaging of all selected coils, and then followed by
a DDD-MRS
diagnostic processor and processing flow that comprises data extraction
related to MRS
spectral regions of diagnostic interest, then applying the diagnostic
algorithm, then
generating a diagnostic patient report.


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[] FIG. 9B shows a schematic flow diagram of further detail of
various
component parts of the DDD-MRS signal processor and respective steps taken
thereby as
shown more generally in FIG. 9A.
[] FIG. 9C shows a schematic flow diagram of further detail of
various
component parts of the DDD-MRS diagnostic processor and processing flow taken
thereby
as also shown more generally in FIG. 9A.
[] FIG. 10 shows a plot of phase angle pre- and post- phase
correction for an
acquisition series example, and as is similarly applied for a DDD-MRS
acquisition such as
for a disc according to certain aspects of the present disclosure.
[] FIG. 11 shows the serial acquisition frame averages for each of 6

individual acquisition channels as shown in FIG. 8, but after phase correction
consistent with
the signal processing flow shown in FIGS. 9A-B and phase-correction approach
illustrated in
FIG.
[] FIG. 12 shows the frame-averaged real part squared MRS spectrum
after
combining the strongest two channels (channels 1 and 2) selected among the 6
phase-
corrected frame-averaged channel spectra shown in FIG. 11 using a channel
selection
approach and criterion according to a further aspect of the current
disclosure, but without
frequency correction.
[] FIG. 13 shows an example of a time-intensity plot for a DDD-MRS
acquisition similar to that shown in FIG. 17D for the acquisition shown in
FIGS. and
12, except that the plot of FIG. 13 relates to another MRS pulse sequence
acquisition series
of another lumbar disc in another subject with corrupted frames midway along
the temporal
acquisition series in order to illustrate frame editing according to other
aspects of the
disclosure.
[] FIG. 14A shows confidence in frequency error estimate vs. MRS
frames
temporally acquired across an acquisition series for a disc, as plotted for
the DDD-MRS
series acquisition shown in different view in FIG.
[] FIG. 14B shows a frame by frame frequency error estimate of the
acquisition series featured in FIG. 14A.
[] FIG. 15 shows all 6 frame-averaged acquisition channels for the
series
acquisition conducted on the disc featured in FIGS. B, prior to
correction.


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[] FIG. 16A shows phase corrected, frequency corrected, but not
frame
edited spectral average combining all of acquired series frames for channels 3
and 4 as
combined after optimal channel selection, for the same series acquisition
featured in FIGS.

[] FIG. 16B shows phase corrected, frequency corrected, and frame
edited
spectral average combining the partial retained frames not edited out from the
acquired series
for channels 3 and 4 as combined after optimal channel selection, also for the
same series
acquisition featured in FIGS.
[] FIG. 17A shows a 2-dimensional time-intensity plot similar to
that shown
in FIG. 13, but for yet another DDD-MRS acquisition series of another disc in
another
subject and to illustrate another mode of frame editing aspects of the present
disclosure.
[] FIG. 17B shows a waterfall plot in 3-dimensions for the DDD-MRS
acquisition series shown in FIG. 17A, and shows the chemical shift spectrum as
a running
cumulative average at discrete points over time of serial frames acquired,
with spectral
amplitude on the vertical axis.
[] FIG. 17C shows an average DDD-MRS spectrum across the full
acquisition series shown in FIGS. 17A-B, without frame editing, and plots both
phase only
and phase + frequency corrected formats of the spectrum.
[] FIG. 17D shows a 2-dimensional time-intensity plot similar to
that shown
and for the same DDD-MRS acquisition series of FIG. 17A, but only reflecting
retained
frames after editing out other frames according to the present aspect of the
disclosure and
referenced to FIGS. 17A-C.
[] FIG. 17E shows a similar waterfall plot of cumulative spectral
averages
and for the same DDD-MRS acquisition series shown in FIG. 17B, but according
to only the
retained frames after frame editing as shown in FIG. 17D.
[] FIG. 17F shows a similar average DDD-MRS spectrum and for the
same
acquisition series shown in FIG. 17C, but only for the retained frames after
frame editing as
shown in various modes in FIGS. 17D-E.
[] FIGS. 18A-B show time-intensity plots of the same MRS series
acquisition for the same disc featured in FIGS. and as pre- (FIG.
18A) and post-
(FIG. 18B) frequency correction according to a further aspect of the present
disclosure, and


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shows each acquisition frame as a horizontal line along a horizontal frequency
range with
brightness indicating signal amplitude (bright white indicating higher
amplitude, darker
indicating lower), and shows the series of related repetitive frames in
temporal relationship
stacked from top to bottom, e.g. top is time zero).
[] FIGS. 19A-B show the same respective time-intensity plots shown
in FIG.
19A (pre-) and FIG. 19B (post-) frequency correction, but in enhanced contrast
format.
[] FIG. 20 shows spectral plots for 6 frame-averaged acquisition
channels for
the same acquisition shown in FIGS. and , except post phase and
frequency
correction and prior to optimal channel selection and/or combination channel
averaging.
[] FIG. 21 shows a spectral plot for phase and frequency error
corrected
channels 1 and 2 selected from FIG. 20 as averaged, according to a further
aspect of the
disclosure.
[] FIG. 22 shows a bar graph of mean values, with standard deviation
error
bars, of Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) pain
scores
calculated for certain of the pain patients and asymptomatic volunteers
evaluated in a clinical
study of Example 1 and conducted using certain physical embodiments of a
diagnostic
system constructed according to various aspects of the present disclosure.
[] FIG. 23 shows a Receiver Operator Characteristic (ROC) curve
representing the diagnostic results of the DDD-MRS diagnostic system used in
the clinical
study of Example 1 with human subjects featured in part in FIG. 22, as
compared against
standard control diagnostic measures for presumed true diagnostic results for
painful vs. non-
painful discs.
[] FIG. 24 shows a partition analysis plot for cross-correlation of
a portion of
the clinical diagnostic results of the DDD-MRS system under the same clinical
study of
Example 1 and also addressed in FIGS. , based on partitioning of the data
at various
limits attributed to different weighted factors used in the DDD-MRS diagnostic
processor,
with "x" data point plots for negative control discs and "o" data point plots
for positive
control discs, also shows certain statistical results including correlation
coefficient ( R2).
[] FIG. 25A shows a scatter plot histogram of DDD-MRS diagnostic
results
for each disc evaluated in the clinical study of Example 1 and also addressed
in FIGS. ,
and shows the DDD-MRS results separately for positive control (PC) discs
(positive on


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provocative discography or "PD+"), negative control (NC) discs (negative on
provocative
discography or "PD-", plus discs from asymptomatic volunteers or "ASY"), PD-
alone, and
ASY alone.
[] FIG. 25B shows a bar graph of the same DDD-MRS diagnostic results

shown in FIG. 25A across the same subject groups of Example 1, but shows the
mean values
with standard deviation error bars for the data.
[] FIG. 26 shows a bar graph of presumed true and false binary
"positive"
and "negative" diagnostic results produced by the DDD-MRS system for painful
and non-
painful disc diagnoses in the clinical study of Example 1, as compared against
standard
control diagnostic measures across the positive controls, negative controls
(including sub-
groups), and all discs evaluated in total in the study.
[] FIG. 27 shows diagnostic performance measures of Sensitivity,
Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV),
and area
under the curve (AUC) which in this case is equivalent to Global Performance
Accuracy
(GPA) for the DDD-MRS diagnostic results in the clinical study of Example 1.
[] FIG. 28 shows a bar graph comparing areas under the curve (AUC)
per
ROC analysis of MRI alone (for prostate cancer diagnosis), MRI+ PROSE (MRS
package
for prostate cancer diagnosis), MRI alone (for discogenic back pain or DDD
pain), and MRI
+ DDD-MRS (for discogenic back pain or DDD pain), with bold arrows showing
relative
impact of PROSE vs. DDD-MRS on AUC vs. MM alone for the respective different
applications and indications, with DDD-MRS results shown as provided under
Example 1.
[] FIG. 29 shows positive predictive value (PPV) and negative
predictive
value (NPV) for MRI alone and for MRI + DDD-MRS (per Example 1 results), both
as
applied for diagnosing DDD pain, vs. standard control measures such as
provocative
discography.
[] FIG. 30A shows a plot of DDD-MRS algorithm output data for a
series of
8 L4-L5 lumbar discs in 8 asymptomatic human control subjects per clinically
acquired and
processed DDD-MRS exam under Example 1, and plots these results twice for each
disc on
first (1) and second (2) separate repeat scan dates in order to demonstrate
repeatability of the
DDD-MRS exam's diagnostic results.


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[] FIG. 30B shows a plot of PG/LAAL ratio data for 3 discs per DDD-
MRS
pulse sequence and signal processing data of Example 1, and shows the
clinically acquired
results via 3T DDD-MRS exams of the discs in vivo in pain patients (y-axis)
against acquired
measurements for the same chemicals in the same disc material but flash frozen
after surgical
removal and using 11T HR-MAS spectroscopy.
[] FIG. 31A shows a digitized post-processed DDD-MRS spectrum (in
phase
real power) as processed according to certain of the MRS signal processor
aspects of the
present disclosure, and certain calculated data derived therefrom as developed
and used for
calculated signal-to-noise ratio (SNR) of the processed result, as taken
across a sub-set of
samples evaluated under Example 1.
[] FIG. 31B shows a digitized pre-processed DDD-MRS spectrum
(absorption) as 6 channel spectral average without deploying the MRS signal
processing
aspects of the present disclosure (e.g. "pre-processing"), and certain
calculated data derived
therefrom as developed and used for calculated signal-to-noise ratio (SNR) of
the processed
result.
[] FIG. 31C shows a scatter plot histogram of signal-to-noise ratio
(SNR) for
standard "all channels, non-corrected" frame averaged MRS spectra (absorption)
produced
by the 3T MR system for a subset of discs evaluated using the DDD-MRS pulse
sequence in
the clinical study of Example 1, and the SNR of MRS spectra (in phase real
power) for the
same series acquisitions for the same discs post-processed by the DDD-MRS
signal
processor configured according to various of the present aspects of this
disclosure, as such
SNR data was derived for example as illustrated in FIGS. 31A-B.
[] FIG. 31D shows the same data shown in FIG. 31C, but as bar graph
showing mean values and standard deviation error bars for the data within each
pre-
processed and post-processed groups.
[] FIG. 31E shows a scatter plot histogram of the ratio of SNR
values
calculated post- versus pre- processing for each of the discs per the SNR data
shown in FIGS.
31C -D .
[] FIG. 31F shows a bar graph of mean value and standard deviation
error
bar of the absolute difference between post- and pre-processed SNR values for
each of the
discs shown in different views in FIGS. 31C-E.


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[] FIGS. 31G-H respectively show the mean and standard deviation for

absolute improvement between pre- and post- processed SNR (FIG. 31F), the mean
ratio
improvement of post-processed/pre-processed SNR (FIG. 31G), and the mean %
improvement of post-processed vs. pre-processed SNR (FIG. 31H).
[] FIG. 32A shows a mid-sagittal T2-weighted MRI image of a patient
evaluated under the clinical study of Example 1 and comparing the diagnostic
results of the
operating embodiment for DDD-MRS system developed according to various aspects
herein
against provocative discography results for the same discs, and shows a number-
coded (and
also may be color coded) diagnostic legend for the DDD-MRS results (on left of
image) and
discogram legend (top right on image) with overlay of the DDD-MRS results and
discogram
results on discs evaluated in the patient.
[] FIG. 32B shows a mid-sagittal T2-weighted MRI image of another
patient
evaluated under the clinical study of Example 1 and comparing the diagnostic
results of the
physical embodiment DDD-MRS system developed according to various aspects
herein
against provocative discography results for the same discs, and shows a number-
coded (and
also may be color coded) diagnostic legend for the DDD-MRS results (on left of
image) and
discogram legend (top right on image) with overlay of the DDD-MRS results and
discogram
results on discs evaluated in the patient.
[] FIG. 33A shows a scatter plot histogram plot of DDD-MRS (or
"Nociscan") diagnostic results against control groups for various discs
evaluated in vivo
according to the data set reviewed and processed under Example 2, as similarly
shown for the
data evaluated in Example 1 in FIG. 25A (plus the further addition of certain
additional
information further provided as overlay to the graph and related to another
aspect of data
analysis applied according to further aspects of the present disclosure under
Example 2).
[] FIG. 33B shows another scatter plot histogram of another
processed form
of the DDD-MRS diagnostic results also shown in FIG. 33A and per Example 2,
after
transformation of the DDD-MRS diagnostic algorithm results for the discs into
"%
probability painful" assigned to each disc as distributed across the positive
(POS) and
negative (NEG) control group sub-populations shown.
[] FIG. 34A shows a scatter plot histogram of signal-to-noise ratio
(SNR) for
standard "all channels, non-corrected" frame averaged MRS spectra (absorption)
produced


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by the 3T MR system for a subset of discs evaluated using the DDD-MRS pulse
sequence
and signal processor in the clinical study of Example 2, and the SNR of
spectra (absorption)
for the same series acquisitions for the same discs post-processed by the DDD-
MRS
processor, as such SNR data was derived for example as illustrated in FIGS.
31A-B.
FIG. 34B shows the same data shown in FIG. 34A, but as bar graph
showing mean values and standard deviation error bars for the data within each
pre-
processed and post-processed groups.
[] FIG. 34C shows a scatter plot histogram of the ratio of SNR
values
calculated post- versus pre-processing for each discs per the SNR data shown
in FIGS. 34A-
B.
[] FIG. 34D shows a bar graph of mean value and standard deviation
error
bar of the absolute difference between post- and pre-processed SNR values for
each of the
discs shown in different views in FIGS. 34A-C.
[] FIG. 34E shows a bar graph of mean value and standard deviation
error
bar of the ratio of post- to pre-processed SNR values for each of the discs
shown in different
views in FIGS. 34A-D.
[] FIG. 34F shows a bar graph of the mean value and standard
deviation
error bar for the percent increase in SNR from pre- to post-processed MRS
spectra for each
of the discs further featured in FIGS. 34A-E.
[] FIG. 35 shows a DDD-MRS spectrum illustrative of a perceived
potential
lipid signal contribution as overlaps with the regions otherwise also
associated with lactic
acid or lactate (LA) and alanine (AL), according to further aspects of the
present disclosure
and as relates to Example 3.
[] FIG. 36 shows a scatter plot histogram of DDD-MRS diagnostic
algorithm
results for the test population of in vivo discs, as calculated for a defined
Group A evaluated
for diagnostic purposes via Formula A, under the Example 3.
[] FIGS. 37A-C show scatter plot histogram of certain embodiments
for the
DDD-MRS diagnostic processor for discs designated as Group B under Example 3,
including
as shown with respect to PG/LAAL ratio results for the discs (FIG. 17A),
logistic regression
generated Formula B results for the discs (FIG. 17B), and the transformed %
probability pain
distribution for the same Group B discs as a result of the results in FIG. 17B
(FIG. 17C).


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[] FIG. 38 shows a scatter plot histogram of certain embodiments for
DDD-
MRS diagnostic processor for discs designated as Group C discs under Example
3, after
applying logistic regression generated Formula C to the DDD-MRS spectral data
acquired for
the group of discs.
[] FIG. 39 shows a scatter plot histogram of another embodiment for
DDD-
MRS diagnostic algorithm, as applied to Group C discs under Example 3
according to a
Formula B "hybrid" illustrative of yet a further embodiment of the present
disclosure.
[] FIGS. 40A-B show MRI images of two lumbar spine phantoms
according
to another Example 4 of the disclosure.
[] FIGS. 40C-D show graphical plots of n-acetyl (NAA) and lactic
acid (LA)
concentrations in discs from phantoms shown in FIGS. 40A-B as measured
according to
certain DDD-MRS aspects of the present disclosure, versus known amounts, per
Example 4.
[] FIGS. 41A-B show schematic flow diagrams of a DDD-MRS exam,
including DDD-MRS pulse sequence, DDD-MRS signal processing, and DDD-MRS
algorithm processing, and various data communication aspects, according to
certain further
aspects of the present disclosure.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[] Previously reported lab experiments used 11T HR-MAS Spectroscopy
to
compare chemical signatures of different types of ex vivo disc nuclei removed
at surgery.
(Keshari et al., SPINE ) These studies demonstrated that certain chemicals
in disc
nuclei, e.g. lactic acid (LA) and proteoglycan (PG), may provide
spectroscopically
quantifiable metabolic markers for discogenic back pain. This is consistent
with other
studies that suggest DDD pain is associated with poor disc nutrition,
anaerobic metabolism,
lactic acid production (e.g. rising acidity), extracellular matrix degradation
(e.g. reducing
proteoglycan), and increased enervation in the painful disc nuclei. In many
clinical contexts,
ischemia and lowered pH cause pain, likely by provoking acid-sensing ion
channels in
nociceptor sensory neurons.
[] The previous disclosures evaluating surgically removed disc
samples ex
vivo with magnetic resonance spectroscopy (MRS) in a laboratory setting is
quite
encouraging for providing useful diagnostic tool based on MRS. However, an
urgent need
remains for a reliable system and approach for acquiring MRS signatures of the
chemical


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composition of the intervertebral discs in vivo in a readily adoptable
clinical environment,
and to provide a useful, clinically relevant diagnostic tool based on these
acquired MRS
signatures for accurately diagnosing discogenic back pain. A significant need
would be met
by replacing PD with an alternative that, even if diagnostically equivalent,
overcomes one or
more of the significant shortcomings of the PD procedure by being non-
invasive, objective,
pain-free, risk-free, and/or more cost-effective. Magnetic resonance
spectroscopy (MRS) is a
medical diagnostic platform that has been previously developed and
characterized for a
number of applications in medicine. Some of these have been approved such as
for example
for brain tumors, breast cancer, and prostate cancer. Some MRS platforms
disclosed have
been multi-voxel, and others single voxel. None of these have been adequately
configured or
developed for in vivo clinical application to reliably diagnose medical
conditions or chemical
environments associated with nociceptive pain, and/or with respect to
intervertebral discs
such as may be associated with disc degeneration and/or discogenic back pain
(including in
particular, but without limitation, with respect to the lumbar spine).
[] Various technical approaches have also been alleged to enhance
the
quality of MRS acquisitions for certain purposes. However, these approaches
are not
considered generally sufficient to provide the desired spectra of robust,
reliable utility for
many intervertebral discs in vivo, at least not at field strengths typically
employed for in vivo
spectroscopy, e.g. from about tesla (T) or about T to about T or
even up to about
7T. Furthermore, while individual techniques have been disclosed for certain
operations that
might be conducted in processing a given signal for potentially improved
signal:noise ratio
(SNR), an MRS signal processor employing multiple steps providing significant
MRS signal
quality enhancement, in particular with respect to improved SNR for multi-
channel single
voxel pulse sequence acquisitions, have yet to be sufficiently automated to
provide robust
utility for efficient, mainstream clinical use, such as in primary
radiological imaging centers
without sophisticated MR spectroscopists required to process and interpret MRS
data. This
is believed to be generally the case as a shortcoming for many such in vivo
MRS exams in
general. Such shortcomings have also been observed in particular relation to
the unique
challenge of providing a robust MRS diagnostic system for diagnosing medical
conditions or
otherwise chemical environments within relatively small voxels, areas of high
susceptibility
artifact potential, and in particular with respect to unique challenges of
performing MRS in


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voxels within intervertebral discs (including with further particularity,
although without
necessary limitation, of the lumbar spine). In solving many of these
challenges according to
certain aspects of the present disclosure, such as those providing particular
utility for
diagnosing discogenic low back pain and/or chemical environments within discs,
additional
beneficial advances have also been made that are also considered more broadly
applicable to
MRS in general, and as may become adapted for many specific applications, as
are also
herein disclosed.
[] Certain aspects of the current disclosure therefore relate to new
and
improved system approaches, techniques, processors, and methods for conducting
in vivo
clinical magnetic resonance spectroscopy (MRS) on human intervertebral discs,
in particular
according to a highly beneficial mode of this disclosure for using acquired
MRS information
to diagnose painful and/or non-painful discs associated with chronic, severe
axial lumbar (or
"low") back pain associated with degenerated disc disease (or "DDD pain"). For
purpose of
helpful clarity in this disclosure, the current aspects, modes, embodiments,
variations, and
features disclosed with particular benefits for this purposed are generally
assigned the label
"DDD-MRS." However, other descriptors may be used interchangeably as would be
apparent
to one of ordinary skill in context of the overall disclosure. It is also
further contemplated
within the scope of this present disclosure that, while this disclosure is
considered to provide
particular benefit for use involving such human intervertebral discs (and
related medical
indications and purposes), the novel approaches herein described are also
considered more
broadly and applicable to other regions of interest and tissues within the
body of a subject,
and various medical indications and purposes. For purpose of illustration,
such other regions
and purposes may include, without limitation: brain, breast, heart, prostate,
GI tract, tumors,
degeneration and/or pain, inflammation, neurologic disorders, alzheimers, etc.
Various aspects of this disclosure relate to highly beneficial
advances in
each of three aspects, and their various combinations, useful in particular
for conducting a
DDD-MRS exam: (1) MRS pulse sequence for generating and acquiring robust MRS
spectra;
(2) signal processor configured to improve signal-to-noise ratio (SNR) of the
acquired MRS
spectra; and (3) diagnostic processor configured to use information from the
acquired and
processed MRS spectra for diagnosing painful and/or non-painful discs on which
the MRS
exam is conducted in a DDD pain patient.


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[] Several configurations and techniques related to the DDD-MRS
pulse
sequence and signal processor have been created, developed, and evaluated for
conducting
3T (or other suitable field strength) MRS on human intervertebral discs for
diagnosing DDD
pain. A novel "DDD" MRS pulse sequence was developed and evaluated for this
purpose,
and with certain parameters specifically configured to allow robust
application of the signal
processor for optimal processed final signals in a cooperative relationship
between the pulse
sequence and post-signal processing conducted. These approaches can be used,
for example,
with a 3 Tesla (3T) "Signa" MR system commercially available from General
Electric (GE).
Highly beneficial results have been observed using the current disclosed
application
technologies on this particular MR platfoini, as has been demonstrated for
illustration
according to Examples provided herein, and it is to be appreciated that
applying the present
aspects of this present disclosure in combination with this one system alone
is considered to
propose significant benefit to pain management in patients requiring
diagnosis. Accordingly,
various aspects of the present disclosure are described by way of specific
reference to
configurations and/or modes of operation adapted for compatible use with this
specific MR
system, and related interfacing components such as spine detector coils, in
order to provide a
thorough understanding of the disclosure. It is to be appreciated, however,
that this is done
for purpose of providing useful examples, and though significant benefits are
contemplated
per such specific example applications to that system, this is not intended to
be necessarily so
limited and with broader scope contemplated. The current disclosure
contemplates these
aspects broadly applicable according to one of ordinary skill to a variety of
MR platforms
commercially available that may be different suitable field strengths or that
may be
developed by various different manufacturers, and as may be suitably adapted
or modified to
become compatible for use with such different systems by one of ordinary skill
(with
sufficient access to operating controls of such system to achieve this).
Various novel and
beneficial aspects of this present disclosure are thus described herein, as
provided in certain
regards under the Examples also herein disclosed.
[] A DDD-MRS sequence exam is conducted according to one example
overview description as follows. A single three dimensional "voxel," typically
a rectangular
= volume, is prescribed by an operator at a control consul, using 3 imaging
planes (mid-
sagittal, coronal, axial) to define the "region of interest" (ROI) in the
patient's body, such as


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shown in FIGS. 1A-C, for MR excitation by the magnet and data acquisition by
the
acquisition channel/coils designated for the lumbar spine exam within the
spine detector coil
assembly. The DDD-MRS pulse sequence applies a pulsing magnetic and
radiofrequency to
the ROI, which causes single proton combinations in various chemicals within
the ROI to
resonate at different "signature resonant frequencies" across a range. The
amplitudes of
frequencies at various locations along this range are plotted along a curve as
the MRS
"spectrum" for the ROI. This is done iteratively across multiple acquisitions
for a given
ROI, typically representing over 50 acquisitions, often or more
acquisitions, and often
between about and about acquisitions, such as between and
acquisitions for
a given exam of a ROI. One acquisition spectrum among these iterations is
called a "frame"
for purpose of this disclosure, though other terms may be used as would be
apparent to one of
ordinary skill. These multiple acquisitions are conducted in order to average
their respective
acquired spectra/frames to reduce the amplitudes of acquired signal components
representing
noise (typically more random or "incoherent" and thus reduced by averaging)
while better
maintaining the amplitudes of signal components representing target resonant
chemical
frequencies of diagnostic interest in the ROI (typically repeatable and more
"coherent" and
thus not reduced by averaging). By reducing noise while maintaining true
target signal, or at
least resulting in less relative signal reduction, this multiple serial frame
averaging process is
thus conducted for the primary objective to increase SNR. These acquisitions
are also
conducted at various acquisition channels selected at the detector coils, such
as for example 6
channels corresponding with the lumbar spine area of the coil assembly used in
the Examples
(where for example 2 coils may be combined for each channel).
[] The 3T MRI Signa system ("Signa" or "3T Signa"), in standard
operation
conducting one beneficial mode of DDD-MRS sequence evaluated (e.g. Examples
provided
herein), is believed to be configured to average all acquired frames across
all acquisition
channels to produce a single averaged MRS curve for the ROI. This unmodified
approach
has been observed, including according to the various Figures and Examples
provided herein,
to provide a relatively low signal/noise ratio, with low confidence in many
results regarding
data extraction at spectral regions of diagnostic interest, such as for
example and in particular
regions associated with proteoglycan or "PG" (n-acetyl) and lactate or lactic
acid (LA).
Sources of potential error and noise inherent in this imbedded signal
acquisition and


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processing configuration of the typical MR system, for example were observed
in conducting
the DDD-MRS pulse sequence such as according to the Examples. These various
sources of
potential error or signal-to-noise ratio (SNR) compromise were determined to
be mostly
correctable - either by altering certain structures or protocols of coil,
sequence, or data
acquisition, or in post-processing of otherwise standard protocols and
structures used.
Among these approaches, various post-acquisition signal processing approaches
were
developed and observed to produce significantly improved and highly favorable
results using
otherwise un-modified operation pre-processing. In particular, various
improvements
developed and applied under the current post-signal processor disclosed herein
have been
observed to significantly improve signal quality and SNR.
[ Certain
such improvements advanced under the post-signal processor
configurations disclosed herein include embodiments related to the following:
(1)
acquisition channel selection; (2) phase error correction; (3) frequency error
correction; (4)
frame editing; and (5) apodization. These modules or steps are typically
followed by channel
averaging to produce one resulting "processed" MRS spectrum, when multiple
channels are
retained throughout the processing (though often only one channel may be
retained). These
may also be conducted in various different respective orders, though as is
elsewhere further
developed frame editing will typically precede frequency error correction. For
illustration,
one particular order of these operations employed for producing the results
illustrated in the
Examples disclosed herein are provided as follows: (1) acquisition channel
selection; (2)
phase correction; (3) apodization; (4) frame editing; (5) frequency
correction; and (6)
averaging.
[] While
any one of these signal processing operations is considered highly
beneficial, their combination has been observed to provide significantly
advantageous results,
and various sub-combinations between them may also be made for beneficial use
and are also
contemplated. Various illustrative examples are elsewhere provided herein to
illustrate
sources of error or "noise" observed, and corrections employed to improve
signal quality.
Strong signals typically associated with normal healthy discs were evaluated
first to assess
the signal processing approach. Signals from the Signa that were considered
more
"challenged" for robust data processing and diagnostic use were evaluated for
further


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development to evaluate if more robust metabolite signal can be elicited from
otherwise
originally poor SNR signals from the Signa.
[] Additional description further developing these aspects according
to
additional embodiments, and other aspects, is provided below.
[] Spine Detector Coil and Patient Positioning
[] A typical DDD-MRS exam according to the present embodiments will
be
conducted in an MR scanner in which the patient lies still in a supine
position with a spine
detector coil underneath the patient's back and including the lower spine.
While this scanner
applies the magnetic and RF fields to the subject, the spine detector coil
functions as an
antenna to acquire signals from resonating molecules in the body. The primary
source of
MRS signals obtained from a Signa 3T MR scanner, according to the physical
embodiments
developed and evaluated in the Examples herein this disclosure, are from the
GE HD CTL
Spine Coil. This is a "receive-only" coil with sixteen coils configured
into eight
channels. Each channel contains a loop and saddle coil, and the channels are
paired into
sections. For lumbar (and thoracic) spine coverage, such as associated with
lumbar DDD
pain diagnosis, sections 4, 5, and 6 are typically deployed to provide six
individual channel
signals, as shown for example in FIG. 2.
[] Defining the Voxel (Voxel Prescription)
[] Certain embodiments of this disclosure relates principally to
"single
voxel" MRS, where a single three dimensional region of interest (ROT) is
defined as a
"voxel" (VOlumetric piXEL) for MRS excitation and data acquisition. The
spectroscopic
voxel is selected based on T2-weighted high-resolution spine images acquired
in the sagittal,
coronal, and axial planes, as shown for example in FIGS. 1A-C. The patient is
placed into
the scanner in a supine position, head first. The axial spine images acquired
are often in a
plane oriented with disc angle (e.g. may be oblique) in order to better
encompass the disc of
interest. This voxel is prescribed within a disc nucleus for purpose of using
acquired MRS
spectral data to diagnose DDD pain, according to the present preferred
embodiments. In
general for DDD-MRS applications evaluating disc nucleus chemical
constituents, the
objective for voxel prescription is to capture as much of the nuclear volume
as possible (e.g.
maximizing magnitude of relevant chemical signals acquired), while restricting
the voxel
borders from capturing therewithin structures of the outer annulus or
bordering vertebral


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body end-plates (the latter being a more significant consideration, where
lipid contribution
may be captured and may shroud chemical spectral regions of interest such as
lactate or
alanine, as further developed elsewhere herein). In fact, the actual operation
may not exactly
coincide with acquiring signal from only within the voxel, and may include
some bordering
region contribution. Thus some degree of spacing between the borders and these
structures is
often desired. These typical objectives may be more difficult to achieve for
some disc
anatomies than others, e.g. relatively obliquely angled discs. For example, L5-
S1 may be
particularly challenging because in some patients it can frequently be highly
angulated,
irregularly shaped, and collapsed as to disc height.
[ In certain voxel prescriptions, the thickness is limited by the
scanner's
ability to generate the magnetic gradient that defines the Z-axis (axial
plane) dimension. For
example, a minimum thickness limit is pre-set to 4mm on the GE Signa 3T. While
such pre-
set limits of interfacing, cooperative equipment and related software may
result in limits on
the current application's ability to function in that environment outside of
these limits, the
broad aspects of the current disclosure should not be considered necessarily
so limited in all
cases, and functionality may flourish within other operating ranges perhaps
than those
specifically indicated as examples herein, such as in cases where such other
imparted
limitations may be released.
[ These usual objectives and potential limitations in mind, typical
voxel
dimensions and volumes (Z-axis, X-axis, Y-axis, Vol) may be for example 5mm
(thick) by
14mm (width) by 16mm (length), and cc, though one may vary any or all of
these
dimensions by operator prescription to suit a particular anatomy or intended
application. The
Z-axis dimension is typically limited maximally by disc height (in order to
exclude the end-
plates, described further herein), and minimally by either the set minimum
limitations of the
particular MR scanner and/or per SAR safety considerations, in many disc
applications (such
as specific indication for pain diagnosis or other assessment of disc
chemistry described
herein). This Z-axis dimension will typically be about 3mm to about 6 mm
(thick), more
typically between about 4mm to about 6mm, and most typically will be suitable
(and may be
required to be, per anatomy) between about 4mm to about 5mm. The other
dimensions are
typically larger across the disc's plane, and may be for example between about
15mm to
about 20mm (width and/or length), as have been observed suitable ranges for
most observed


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cases (e.g. per the Examples herein). While the higher dimension of these
ranges is typically
limited only by bordering tissues desirable to exclude, the opportunity for
patient motion to
alter the relative location of the target voxel relative to actual anatomy may
dictate some
degree of "spacing" from such bordering structures to ensure exclusion. The
smaller
dimensions of the ranges are more related to degraded signal quality that
comes with
excessively small voxel volume, whereas signal amplitude will typically be
directly related to
voxel dimension and volume. Accordingly, voxels within discs will generally
provide robust
results, at least with respect to signal quality, at volumes of at least about
.5cc, and in many
cases at least about cc or 1 cc. This typically will be limited by
bordering anatomy to up to
about 2ccs, or in some less typical cases up to about 3ccs for exceptionally
large discs.
These voxel volume ranges will typically be achieved with various combinations
of the
typical axis dimensions as also stated above.
[] Also according to the typical voxel prescription objectives and
limitations
stated above, an initial prescription may not be appropriate for achieving
acceptable results,
though this may not be known until a sequence is begun to allow observation of
acquired
signal quality. Accordingly, further aspects of the present disclosure
contemplate a voxel
prescription protocol which prescribes a first prescription, monitors results
(either during
scan or after completion, or via a "pre-scan" routine for this purpose), and
if a lipid signature
or other suspected signal degradation from expected results is observed, re-
prescribe the
voxel to avoid suspected source of contaminant (e.g. make the voxel smaller or
adjust its
dimensions, tilt, or location) and re-run an additional DDD-MRS acquisition
series (retaining
the signal considered more robust and with least suspected signal degradation
suspected to be
voxel error). According to still a further mode, a pre-set protocol for re-
prescribing in such
circumstances may define when to accept the result vs. continue re-trying. In
one
embodiment, the voxel may be re-prescribed and acquisition series re-run once,
or perhaps
twice, and then the best result is to be accepted. It is to be appreciated, as
with many
technology platforms, that operator training and techniques in performing such
user-
dependent operations may be relevant to results, and optimal (or conversely
sub-optimal)
results may track skill levels and techniques used.
[] To further illustrate this current aspect of the present
disclosure, the
example of a single voxel prescription according to the typical three planar
slice images is


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shown in Figures 1A-1C as follows. More specifically, Fig. 1 A shows a coronal
view
oriented aspect of the voxel prescription. Fig. 1B shows a sagittal view
oriented aspect of the
voxel prescription. Fig. 1C shows an axial view oriented aspect of the voxel
prescription.
[] The "DDD" MRS Pulse Sequence - PRESS
[ The DDD-MRS pulse sequence according to one embodiment shares
certain similarities, though with certain differences and modifications
defined herein, with
another MRS pulse sequence called "PROSE". PROSE is primarily intended for use
for
diagnosing prostate cancer, and is approved for use and sale and available
from GE on T
GE MR systems. The DDD-MRS pulse sequence of the present embodiments, and
PROSE
for further reference, employ a sequence approach called Point RESolved
Spectroscopy
(PRESS). This involves a double spin echo sequence that uses a excitation
pulse with
two slice selective refocusing radio frequency (RF) pulses, combined with
3D chemical
shift imaging (CSI) phase encoding gradients to generate 3-D arrays of
spectral data or
chemical shift images. Due to the small size, irregular shape, and the high
magnetic
susceptibility present when doing disc spectroscopy for DDD pain, the 3D phase
encoding
option available under PROSE is not an approach typically to be utilized under
the current
disclosed version of DDD-MRS sequence, and single voxel spectra are acquired
by this
version embodiment of DDD-MRS pulse sequence. This unique relative
configuration for
the DDD-MRS pulse sequence can be accomplished by setting the user control
variables
(CVs) for the matrix acquisition size of each axis to 1 (e.g., in the event
the option for other
setting is made available). Further aspects of pulse sequence approaches
contemplated are
disclosed elsewhere herein. It is to be appreciated that while the modified
PRESS approach
herein described is particularly beneficial, other approaches may be taken for
the pulse
sequence according to one of ordinary skill consistent with other aspects and
objectives
herein described and without departing from the broad aspects of intended
scope herein.
[] Water and Lipid Signal Suppression - CHESS
[] In another sequence called "PROBE" also commercially available
from
GE, and which is a CSI sequence used for brain spectroscopy, the lipid/fat
signals are
believed to be resolved through the use of long TE (ms) periods and 2
dimensional
transformations (2DJ). These acquisition and signal processing techniques are
believed to be
facilitated by the large voxel volumes prescribed in the brain as well as the
homogeneity of


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the brain tissue resulting in relatively narrow spectral line widths. In the
prostate region
targeted by the different pulse sequence of PROSE, however, the voxel
prescriptions are
much smaller and it is often impossible to place the voxel so as to assuredly
exclude tissues
that contain lipid/fat. Therefore, two water and lipid suppression approaches
are available
and may be used, if warranted, in the PROSE sequence: "BASING" and "SSRF"
(Spectral
Spatial Radio Frequency). An even more challenging environment of bordering
lipid and
reduced homogeneity has been observed with the current DDD pain application of
the
lumbar intervertebral discs where the current ROT within disc nuclei are
closely bordered by
vertebral bodies with bone marrow rich in lipid content. However, due both to
the desire to
use short TE times (e.g. 28ms) for the current DDD pain application in lumbar
spine, and the
desire to observe MRS signatures of other chemicals within disc nuclei that
may overlap with
lipid signal contribution along the relevant DDD-MRS spectrum, these
water/lipid
suppression approaches as developed for brain and prostate application are not
necessarily
optimized for DDD-MRS application in many circumstances. While a SSRF
suppression
approach for lipid resonances may be employed in the DDD-MRS sequence, the
narrow band
RF pulse required for this may require a long RF period and amplitude that
will exceed the
SAR level for many MR systems.
[] Water suppression is also provided by a CHESS sequence
interleaved or
otherwise combined in some manner with the PRESS sequence in order to provide
appropriate results. Optimization of the residual water spectral line for
frequency correction
is done, according to one highly beneficial further aspect of the present
disclosure, with the
setting prescribed for the third flip angle. The angle is lowered to reduce
the water
suppression function which increases the residual water spectral line
amplitude. Conversely
higher relative third flip angles will increase water suppression for reduced
water signal in an
acquired MRS spectrum. A particular flip angle for this purpose may be for
example about
, though may be according to other examples between about 45 degrees and
about
degrees (or as much as about degrees). Accordingly, in one aspect, the
flip angle may be
for example at least about 45 degrees. In another aspect, the flip angle may
be up to
degrees or even up to degrees. Notwithstanding these examples, an expanded

experimental data set of 79 discs in 42 subjects represented under Example 3
included robust,
reliable results across this population with an average flip angle of about
or degrees


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+/- about 33 degrees. Moreover, a later component of that population conducted
with further
refinements revealed a majority of cases suitable at a flip angle between
about 65 degrees
and about degrees, and in fact with most found to be sufficient at about
85 degrees. It is
to be appreciated that despite these specific number and range examples, and
robust results
observed therefrom, it is also believed that flip angles within about 5 or 10
degrees apart are
likely to produce susbstantially similar results for purposes intended herein.
[] This flip angle aspect of the present disclosure is another
example where
some degree of customization may be required, in order to optimize water
signal for a given
disc, in a given particular MR system. As some discs may be more dehydrated or
conversely
more hydrated than others, the water suppression may be more appropriate at
one level for
one disc, and at another level for another disc. This may require some
iterative setting and
acquisition protocol to optimize, whereas the angle example described herein
is considered
appropriate for most circumstances and may be a pre-defined starting place for
"first try."
[] For further clarity and understanding of the present DDD-MRS
pulse
sequence embodiments introduced above and also elsewhere herein described,
FIG. 3A
shows an example of a CHESS water suppression pulse sequence diagram, whereas
FIG. 3B
shows an example of a combined CHESS - PRESS pulse sequence diagram.
[] Very Selective Saturation (VSS) Bands
[] The volume excitation achieved using PRESS takes advantage of
three
orthogonal slices in the form of a double spin echo to select a specific
region of interest. In
some embodiments, the range of chemical shift frequencies (over Hz for
proton at T)
is not insignificant relative to the limited band width of most excitation
pulses (
Hz). The result can be a misregistration of the volume of interest for
chemical shift
frequencies not at the transmitter frequency. Thus, when a PRESS volume is
resolved by
MRS, the chemical levels may be not only dependent on tissue level, T1 and T2,
but also
dependent on location within the volume of interest. In some embodiments, due
to
imperfections in the RF pulse, out of volume excitation may occur which can
present signals
from chemicals that are not in the frequency/location range of interest.
[] Accordingly, another feature that is contemplated according to a
further
mode of the DDD-MRS sequence is the use of very selective saturation (VSS)
pulses. This
is often beneficial to deploy for example for removal of signal contamination
that may arise


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from chemical shift error due to the presence of lipids within the voxel as
well as outside the
selected ROT or voxel in the disc nuclei. In the default operating mode of one
DDD-MRS
sequence approach, in some regards sharing some similarities with PROSE, for
example,
multiple pairs of VSS RF suppression bands are placed symmetrically around the
prescribed
DDD-MRS voxel. In certain embodiments, the voxel in this approach is
oversized, such as
for example by % (e.g. PRESS correction factor = about ). The DDD-MRS
sequence
according to this mode uses the VSS bands to define the actual DDD-MRS volume.
It is
believed that up to about six additional VSS bands may be prescribed (each
consisting of
three VSS RF pulses) graphically in PROSE, with the goal of reducing the
chemical shift
error that can occur within the voxel as well as suppress excitation of out of
voxel tissue
during the PRESS localization of the voxel. According to some observations in
applying
DDD-MRS to disc spectroscopy, these additional graphic VSS pulses were found
to not
significantly improve the volume selection. In other observations, some
benefit is suspected
to have resulted. Accordingly, while they may provide benefit in certain
circumstances, they
also may not be necessary or even desired to be used in others.
[] FIG. 3C shows a schematic diagram of certain aspects of a
combined
CHESS ¨ VSS- PRESS pulse sequence diagram also consistent with certain aspects
of the
present disclosure. As also shown for further illustration in multiple
respective image planes
in FIGS. 4A-B, multiple VSS bands are placed around the voxel prescription in
each plane to
reduce out of voxel excitation and chemical shift error present during the
PRESS localization
of the voxel.
[] PRESS timing parameters
[] For purpose of comparative reference, the echo time (TE) of about
ms
is believed to be the default selection typically used for PROSE data
acquisitions. This echo
time is typically considered too long for DDD-MRS pulse sequence applications
for
acquiring robust disc spectra due to the small voxel volume and shorter T2
relaxation times of
the chemical constituents of lumbar intervertebral discs, leading to a
dramatic decrease in
signal to noise in long echo PRESS spectra. Therefore a shorter echo time
setting for the
scanner, such as for example about 28 milliseconds, is generally considered
more appropriate
and beneficial for use in the current DDD-MRS sequence and DDD pain
application (though
this may be varied as would be apparent to one of ordinary skill based upon
review of this


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total disclosure and to suit a particular situation). A frame repetition time
(TR) of for
example about ms provides sufficient relaxation of the magnetic dipoles
in the ROT and
leads to reasonable acquisition times and is believed to represent a
beneficial compromise
between short acquisition times and signal saturation at shorter values of TR
(though this
may also be varied, as also elsewhere herein described). Other appropriately
applicable
operating parameter settings for PRESS spectra suitably applicable to the DDD-
MRS
sequence may be, for example: number of data points equal to about ,
number of
repetitions equal to about , and example typical voxel size of about 4mm x
18mm x
16mm (cc). Furthermore, first, second, and third flip angles of PRESS for
the current
DDD-MRS sequence embodiment may be for example 90, , and , respectively
(though these may slightly vary, and user-defined settings may not always
reflect actual
angle ¨ for example the latter two values may be exchanged with or represent
one example of
an actual result of a degree setting).
[] Summary of User Control Variable (CV) Examples for DDD-MRS
Sequence
[] The foregoing disclosure describes various user controllable
sequence
settings observed to be appropriate and of particular benefit for use in an
example DDD-
MRS sequence according to the current disclosure and for use for diagnosing
DDD pain, as
contemplated under the preferred embodiments herein. These are further
summarized in
Table 1 appended herewith at the end of this disclosure.
[] One or more of these CVs may comprise modifications from similar
settings that may be provided for another CHESS-PRESS or CHESS-VSS-PRESS pulse

sequence, such as for example PROSE, either as defaults or as user defined
settings for a
particular other application than as featured in the various aspects herein
this disclosure.
These CV settings, in context of use as modifications generally to a sequence
otherwise
sharing significant similarities to PROSE, are believed to result in a highly
beneficial
resulting DDD-MRS sequence for the intended purpose of later signal
processing, according
to the DDD-MRS signal processor embodiments herein described, and performing a

diagnosis of DDD pain in discs examined (the latter according for example to
the DDD-MRS
diagnostic processor aspects and embodiments also herein disclosed). However,
it is also
appreciated that these specific settings may be modified by one of ordinary
skill and still


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provide highly beneficial results, and are also contemplated within the broad
intended scope
of the various aspects of this present disclosure.
[] Data Acquisition of DDD-MRS Pulse Sequence
[] The signal detected in the MR spectrometer in the receiving
"detector"
coil assembly, after exposing a sample to a radio frequency pulse, is called
the Free
Induction Decay (FID) for purpose of this disclosure. In modem MR
spectrometers the MR
signal is typically detected using quadrature detection. As a result, the
acquired MR signal is
composed of two parts, often referred as real and imaginary parts of the FID.
A schematic
example of the time domain FID waveform is shown in FIG. 5, which shows the
real (Sx)
and imaginary (Sy) parts of an FID (right) that correspond to x and y
components of the
rotating magnetic moment M (left).
[] FIDs are generated at the period defined by TR. Thus a TR of
about
milliseconds, according to the example embodiment described above, equals a
rate of about 1
Hz (about one FID per second). The FID signal received from each coil channel
is digitized
by the scanner to generate a point complex number data set or acquisition
frame. An
MRS scan session consists of a number of frames of unsuppressed water FIDs
(such as for
example may be about 16 frames) and up to or more (as may be defined by an
operator
or setting in the pulse sequence) frames of suppressed water FIDs, which
together are
considered an acquisition series. The unsuppressed water FIDs provide a strong
water signal
that is used by the signal processing to determine which coils to use in the
signal processing
scheme as well as the phase information from each coil (and in certain
embodiments may
also be used for frequency error correction). However, due to gain and dynamic
range in the
system these high water content unsuppressed frames do not typically provide
appropriate
resolution in the target biomarker regions of the associated spectra to use
them for diagnostic
data purposes. The suppressed water FIDs are processed by the DDD-MRS
processor to
obtain this spectral information, although the unsuppressed frames may be used
for certain
processing approaches taken by the processor.
[] For further illustration, FIG. 6 shows a plot of all the FIDs
obtained in a
DDD-MRS pulse sequence scan according to certain present illustrative
embodiments, and is
an amplitude plot of complex data from a standard DDD-MRS acquisition with the
y-axis


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representing the magnitude of FID data and the x-axis representing serial
frame count over
time.
[] DDD-MRS Pulse Sequence Data Transfer from MR System to Post-
Processor
[] The MR scanner generates the FIDs using the defined sequences to
energize the volume of interest (VOI), digitizes them according to the defined
data
acquisition parameters, and stores the data, typically as floating point
numbers. While this
data may be packaged, e.g. in "archive file," and communicated in various
formats and
methods, one example is provided here. A data descriptor header file (DDF)
with all the
aforementioned parameters along with voxel prescription data is appended to
the data to
generate the archive file. Examples of certain parameters provided in a DDF,
are as
follows:studyID (String); seriesNum (Integer for assigned Series Number);
studyDate(String
date code); seriesDesc (String for series description); rootName (String);
nSamps (Integer for
number of complex samples, typically ); nFrames (Integer for number of
frames or
reps); coilName (String); pulseSeqName (String); Te (Float, echo time, in ms);
Tr (Float,
repetition time, in ms); TxFreq (Float, in MHz); nSatBands (Integer, number of
saturation
bands); voxTilt (Float, voxel tilt about x-axis, in degrees); voxVol (Float,
Voxel volume in
cc); voxX (Float, Voxel X dimension, in mm); voxY (Float, Voxel Y dimension,
in mm);
voxZ (Float, Voxel Z dimension, in mm). The archive file can include data
received from
the MR scanner that is representative of the anatomy of a patient (e.g.,
representative of the
chemical makeup of tissue inside the area of interest inside an intervertebral
disc of the
patient's spine).
[] The archive file may then be transferred to another computer
running an
application written in a language, such as for example Matlab Ra (e.g.
with "Image
Processing Toolbox" option, such as to generate time-intensity plots such as
shown in
various Figures herein), which opens the archive file. The Matlab application
may be user-
configurable, or may be configured as full or partial executables, and is
configured to signal
process the acquired and transferred DDD-MRS information contained in the
archive file,
such as according to the various signal processing embodiments herein. Other
software
packages, such as "C," "C+," or "C++" may be suitably employed for similar
purposes. This
application, subsequently referred to as the DDD-MRS signal processor, parses
information


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pertinent to the signal processing of the data from the data description
header, and imports
the FID data acquired at each detector coil for subsequent signal processing.
It will be
understood that the DDD-MRS signal processor can be implemented in a variety
of manners,
such as using computer hardware, firmware, or software, or some combination
thereof In
some embodiments, the DDD-MRS signal processor can comprise a computer
processor
configured to execute a software application as computer-executable code
stored in a non-
transitory computer-readable medium. In some embodiments, the computer
processor can be
part of a general purpose computer. In some embodiments, the DDD-MRS signal
processor
can be implemented using specialized computer hardware such as integrated
circuits instead
of computer software. It will be understood that the DDD-MRS signal processor,
as well as
other components described herein that may be implemented by a computer, can
be
implemented by multiple computers connected, for example, by a network or the
internet.
Thus, algorithms, processes, sequences, calculations, and tasks, etc.
described herein can be
divided into portions to be performed by multiple computer processors or other
hardware
located on multiple physically distinct computers. Also, some tasks that are
described herein
as being performed by distinct computers or systems may be performed by a
single computer
or a single integrated system.
[] The archive file and related MRS data may be communicated via a
number of available networks or methods to external source for receipt,
processing, or other
form of use. In one particular typical format and method, the information is
communicated
via picture archiving and communication system (PACS) that has become
ubiquitous for
storing and communicating radiologic imaging information. In addition to the
archive file
with DDF and stored MRS data, accompanying MRI images may also be stored and
communicated therewith, e.g. in standardized "digital imaging and
communications in
medicine" or "DICOM" format.
[] The data transfer described may be to a local computer processor
for
processing, or more remotely such as via the web (typically in secure format).
In alternative
to data transfer of acquired MRS data pre-processing to an external system for
post-
processing as described above, e.g. MRS signal processor and diagnostic
processor aspects of
this disclosure, all or a portion of the various aspects of the present
embodiments may be
installed or otherwise integrated within the MR system itself, e.g. a computer
based


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controller or processor embedded therewithin or otherwise connected thereto,
for operation
prior to packaging results for output (and any remaining portions might be
performed
peripherally or more remotely).
[] DDD-MRS Signal Processing
[] Upon the acquisition of all MRS data from a DDD-MRS pulse
sequence
exam, according to certain aspects of the present embodiments, the MR scanner
system will
typically provide the operator with a spectral image that is the averaged
combination of all
frames across all the 6 detection channels (coils). An example of such a
waveform from an
MRS pulse sequence exam acquired for an ROI in a disc nucleus via a GE Signa
3T MR
system is shown in FIG. 7, which shows a typical scanner-processed spectral
signal plot of
combined, averaged channels. FIG. 8 shows the magnitude only (no correction)
MRS
spectral images of each of the six channels which are aggregated to form the
output from the
example MR system as shown in FIG. 7, and thus this raw uncorrected individual
channel
spectral data output provides the input to the DDD-MRS signal processor of the
present
embodiments.
[] According to one highly beneficial mode, the DDD-MRS signal processor
is
configured to conduct a series of operations in temporal fashion as described
herein, and as
shown according to the present detailed embodiments in the flow charts
illustrated in general
to increasing detail for the various component modes and operable steps in
FIGS. 9A-C.
More specifically, FIG. 9A shows a general schematic overview for the flow of
a diagnostic
system 2 that includes a signal processor 4 and a diagnostic processor 6.
Signal processor
includes various sub-components and processors that carry out certain steps,
such as a
channel selector that conducts channel or "coil" selection step 10, phase
corrector that does
phase correction step 20, apodizer that conducts apodizer step 30, domain
transformer that
conducts the domain transform step 44 such as from time domain to frequency
domain,
frame editor that conducts frame editing steps 50, frequency corrector that
conducts
frequency correction steps 60, and channel combiner that conducts combining or
averaging
steps 70 to aggregate retained channels into one final post-processed spectral
results (not
shown). Following signal processing steps 6, a diagnostic processor conducts
diagnostic
processing of the signal processed signals through data extraction steps ,
diagnostic
algorithm application steps , and patient or diagnostic report generation
While this


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configuration is considered highly beneficial, these same or similar tasks may
be performed
in different order, as would be apparent to one of ordinary skill.
[] For illustration, FIGS 9B-C show further details regarding some of
these
specific steps, and also illustrate a different order than has been shown and
referenced to
FIG. 9A. More specifically, the signal processor 4 in shown to include the
main primary
steps shown in FIG. 9A, but in finer detail and different order. It some
embodiments, the
steps shown in FIGS. 9A-C may be performed in an order different than those
shown in
FIGS. 9A-C. Also, in some embodiments, steps that are shown in FIGS. 9A-C can
be
omitted, combined with other steps, or divided into additional sub-steps.
Additionally, in
some cases, additional steps not specifically shown in FIGS. 9A-C can be
performed in
addition to the steps shown in FIGS. 9A-C.
[] Channel selection includes the following steps: signal power
measurement
step 11 measures signal power for SNR calculation, as shown here in the
specific
embodiment in first points of FID with unsuppressed water. Noise power
measurement
step 13 measures noise in the last points, for example, of the FID with
unsuppressed
frames. SNR estimate 15 is then conducted, at which point thereafter channel
selection step
17 is conducted per the channel with the maximum or highest signal. Channel
selection
includes an additional step 18 where additional channels are selected if
within range of the
strongest, e.g. about 3dB. Upon completing channel selection, an index of
selected channels
is generated (step 19).
[] Frame editing operation 50 is also shown, with transformation of
unsuppressed water frame to frequency domain 51, locate water peak in +1- 40Hz
per peak
location step 53, frame confidence level calculation 55, frame frequency error
and store 57,
and actual frame selection step 59 based upon minimum confidence level
threshold (e.g. .8)
Phase correction 20 is also done per applying 21 1st order linear curve fit to
the FID of each
unsuppressed water frame (e.g. n=16)., obtaining average of zero order terms
from the curve
fit 23, and rotate 25 all suppressed water frame FIDs by zero order term.
Apodization 30
includes for each selected channel and each frame indexed for frequency
correction 31, then
apply point boxcar function to the FID (step 33). In addition, frequency
correction 60
entails for each selected channel and each frame indexed for frequency
correction and
apodization 61, transform 63 the frame (FID) to the frequency of domain, and
locate the


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frequency error 65 for the frame as identified during frame editing. The
spectrum is shifted
67 by the frequency error value to frequency correct the spectrum. Step 69
adds frequency
corrected spectrum to many to spectral average for all selected channels.
[] As also shown in FIG. 9C for the diagnostic processor 6, data
extraction
involves opening the acquisition spectral average lot generated in
frequency correction,
scan step of spectral plot for metabolite features and apply to bins, and
record data bins
to acquisition metabolite The diagnostic algorithm itself involves
opening the
acquisition metabolite file extracted from spectral average , extract
metabolite
features required by diagnostic linear regression equations, and generate
acquisition
diagnostic score and store to file. Report generation includes open
acquisition diagnostic
score fill , open DICOM file for Patient ID associated with acquisition
diagnostic
score, extract sagittal image from the DICOM report, apply acquisition
score to
sagittal image for each scanned disc, and return sagittal image to DICOM
report
[] According to the current example embodiment, a first operation of the
DDD-
MRS processor assesses the SNR of each channel. This is done to determine
which channels
have acquired sufficiently robust signal to use for data processing and
averaging. The result
may produce one single channel that is further processed, or multiple channels
that are later
used in combination under multi-channel averaging. In the majority of acquired
signals
observed according to the Examples disclosed herein, only a subset of the 6
lumbar
acquisition channels were determined to be sufficiently robust for use.
However, the
standard system output averages all 6 channels. Accordingly, this filtering
process alone ¨
removing poor signal channels and working with only stronger signal channels ¨
has been
observed to dramatically improve processed spectra for diagnostic use in some
cases. While
various techniques may be suitable according to one of ordinary skill, and
thus contemplated
herein, according to the present illustrative embodiment the SNR is calculated
by obtaining
the average power in the first data points (the signal) and the last
points (the noise)
of the unsuppressed water FID. The unsuppressed water FIDs signals are used
because of the
strong water signal. The channel with the greatest SNR, and channels within a
predetermined
threshold variance of that strongest one, e.g. within about 3 dB for example,
are preserved for
further processing and as candidates for multi-channel averaging ¨ other
channels falling


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below this range are removed from further processing (though may be used for
further
processing, yet removed from final results).
Further examples and embodiments for evaluating relative channel
quality are
provided as follows. One additional indication of channel quality that may be
observed and
used is the line width of the unsuppressed water signal based on the averaged
frequency and
phase corrected FFTs of the coil channels with the highest SNRs. This is
computed to serve
as a general indicator of signal quality as determined by the quality of the
shimming process
and to provide an estimate of the resolution we should expect in the chemical
shift spectrum.
Another indication of channel quality is the degree of water suppression. This
has utility in
determining the optimum degree of water suppression to apply in the
acquisition protocol.
The water suppression should leave enough residual water signal to use as a
reference to
reliably perform frame-by-frame frequency correction but not so much that
water signal
artifacts affect the chemical shift spectrum in the metabolite areas of
interest. Such artifacts
include simple spectral leakage as well as phase modulation sidebands due to
gradient
vibration induced Bo modulation.
[] Further to the present embodiments and per further reference to
FIGS. 9A-
B, a second operation conducted by the DDD-MRS processor is phase alignment,
or phase
error correction. This is performed to support coherent summation of the
signals from the
selected channels and the extraction of the absorption spectra. This is often
necessary, or at
least helpful, because in many cases a systemic phase bias is present in the
different
channels. This systemic phase bias is best estimated by analysis of the data
frames (e.g.
about 16 frames in the DDD-MRS pulse sequence of the current illustrative
detailed
embodiments) collected at the beginning of each scan without water
suppression. This
operation, according to one mode for example, analyzes the phase sequence of
the complex
samples and fits a polynomial to that sequence. A first-order (linear) fit is
used in one further
illustrative embodiment. This is believed to provide a better estimate of the
offset than
simply using the phase of the first sample, as is often done. This is because
eddy current
artifacts, if present, will be most prominent in the first part of the frame.
The offset of the
linear fit is the initial phase. Observation has indicated that the first
samples (75 mS at
the typical samples-per-second rate) typically provide reliable phase
data. The fit is
performed on each of the water-unsuppressed frames for each channel and the
mean phase of


CA
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these is used to phase adjust the data for the corresponding channel. This is
accomplished by
performing a phase rotation of every complex sample in each frame to
compensate for the
phase offset as estimated above, setting the initial phase to zero.
[] The offset of the linear fit is the phase bias with respect to
zero and the
slope is the frequency error with respect to perfect center-tuning on the
water signal. Only the
offset portion of the curve fit is used to phase correct the data. An
illustrative example of this
is shown in schematic form in FIG. 10, which shows phase angle before and
after phase
correction. The phase angle signal is shown as the dotted line. The solid line
is the least
squares fit estimate. The dashed line is the phase and frequency corrected
signal, though the
offset component is used to phase correct and frequency correction is
performed
subsequently in the temporal process according to the present DDD-MRS
processor
embodiment.
[] The real-part squared MRS spectral results of phase correction
for each of
all the six channels shown prior to correction in FIG. 8 is shown in FIG. 11,
with channels 1-
3 indicated from left to right at the top, and channels indicated from
left to right at the
bottom of the figure. The averaged spectrum of the selected, phase corrected
channels
(channels 1 and 2) is shown in FIG. 12, which reflects significant SNR
improvement versus
the uncorrected all channel average spectrum shown in FIG. 7.
[] Frame Editing
[] While it is contemplated that in some circumstances individual
MRS
acquisition frames may provide some useful information, frame averaging is
prevalently
indicated in the vast majority of cases to achieve a spectrum with sufficient
SNR and
interpretable signal at regions of interest for pathology assessment. It is,
at most, quite rare
that an individual frame will have sufficient SNR for even rudimentary
metabolite analysis to
the extent providing reliable diagnostic information. Often individual frames
along an
acquisition= series will have such low SNR, or possess such artifacts, that
they make no
improvement to the average - and in fact may even degrade it. To the extent
these "rogue"
frames may be recognized as such, they may be excluded from further processing
¨ with only
robust frames remaining, the result should improve.
[] Accordingly, a further mode of the present DDD-MRS processor
embodiment utilizes a frame editor to conduct frame editing to identify those
frames which


CA
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vary sufficiently from the expected or otherwise observed acquisition results
such that they
should be excluded, as is also represented schematically in the flow diagram
examples of
FIGS. 9A-B. In one aspect of the underlying concern, certain patient motions
during an
acquisition may result in signal drop-out as well as frequency shifts (e.g.
magnetic
susceptibility artifact). While involuntary motion, e.g. respiration, is a
common cause of
frequency shifts, these are typically sufficiently minor and within a range
that they are not
believed to implicate signal quality other than the shift itself (which can be
significant source
of SNR degradation, but correctable per the present disclosure). However,
other more
significant movements (e.g. voluntary) may cause sufficiently significant
shifts to seriously
degrade the acquired spectrum, beyond merely correctable spectral shifts. For
example, such
activity may move the voxelated region to include adjacent tissues versus only
the intended
VOI upon prescription prior to the motion. If the salient artifact is
frequency shift, a
correction may be applied and the frame can be used to make a positive
contribution to the
averaged spectrum. If a frame is discarded its contribution is lost, and
across sufficient
number of discarded frames across a series the result may not include a
sufficient number of
frames in the average for a reliable SNR in the resulting spectrum. The DDD-
MRS
processor, according to the current embodiment, analyzes the residual water
signal in each
frame to determine if it is of sufficient quality to support frequency
correction.
[] FIG. 13 shows a time-intensity plot which illustrates a scan
series with
frequency shifts and "drop outs" with SNR changes considered to represent
corrupted frames
due to patient motion. More specifically, this shows 1 dimensional horizontal
lines for each
frame, with signal amplitude reflected in "brightness" or intensity (e.g.
higher values are
whiter, lower are darker), with time across the serial acquisition of the
series progressing top
to bottom vertically in the Figure. A vertical band of brightness is revealed
to the left side of
the plot. However, in this particular example, there is a clear break in this
band as "drop out"
frames. After excluding the "drop out" frames (center of time sequence between
about 75
and MRS frames, it was still possible to obtain a high quality final
averaged spectrum
from this scan using the remaining robust frames, as further developed
immediately below.
[] FIGS. 14A and 14B show the confidence level estimate and the
frame by
frame frequency error estimate, respectively, which are used according to the
present
embodiment for frame editing according to this acquisition series example of
FIG. More


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specifically, FIG. 14A shows the frame by frame confidence level, with
confidence level on
the Y-axis, and the sequential series of frame acquisitions along a scan
indicated along the X-
axis. FIG. 14B shows the actual frequency error along the Y-axis, for the same
frame series
along the X-axis. This is based on analyzing the characteristics of the
residual water peak
and the noise in a band 80 Hz wide (for 3T processing, the band would be 40 Hz
wide at
T) around the center-tuned frequency. The largest peak is assumed to be the
water signal
and the assumption is qualified by the confidence estimate. For the purpose of
this example,
if the confidence value is above a threshold, i.e. , the frame is flagged
as a candidate for
frequency correction and thus "retained." As seen from the plots in FIGS. 14A-
B, when the
confidence is low, the variance of the frequency error estimate is greatly
increased. The final
qualification step, per this example, is to determine if there are enough
qualified candidate
frames to achieve sufficient SNR improvement when averaged. This threshold
limit for
proceeding with frequency correction (and thus frame editing therefore) has
been empirically
established as 90 frames meeting the criteria. According to the present
embodiments, this
has been observed to provide sufficiently robust results per the Examples
described herein. It
is to be appreciated, however, that other limits may be appropriate in various
circumstances.
The number of frames required will be based upon the SNR levels achievable
from the
completed signal processing. This will be paced by SNR of input signal
acquisitions to begin
with, and performance of other signal processing modes and steps taken with
those signals.
According to the acquisitions under the Examples disclosed herein, SNR is
believed to
increase over about frames, and then with little gained typically
thereafter, though the 90
frame minimum limit has been observed to provide sufficient results when
reached (in rare
circumstances). In the event the result drops below the 90 frame limit, the
DDD-MRS
processor is still configured to proceed with other modes of signal
processing, signal quality
evaluation, and then diagnostic processor may be still employed ¨ just without
the added
benefit of the frame editing and frequency error correction.
[] Further description related to acceptable confidence level
estimate
approach according to the present disclosure is provided as follows, for
further illustration of
this embodiment for the frame editing and frequency correction modes of the
disclosure. The
discrete amplitude spectrum can be analyzed in the range of the center-tuned
frequency 40
Hz for example in the case of a 3T system acquisition, and half this bounded
range (e.g. 20


CA
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PCT/US/
Hz) for a T system acquisition. The highest peak is located to determine
its width at the
half-amplitude point. Next, the total spectral width of all parts of the
spectrum which exceed
the half-amplitude point of the highest peak are determined. The confidence
estimate is
formed by taking the ratio of the spectral width of the greatest peak divided
by the total
spectral width which exceeds the threshold. If there is only a single peak
above the
threshold, the confidence estimate will be , if there are many other peaks
or spectral
components which could be confused with the greatest one, then the estimate
will approach
0Ø This provides a simple and robust estimate of the randomness or dispersal
of energy in
the vicinity of the water peak. Like another approach using entropy
measurement, e.g. as
described below, this current approach provides at least one desirable
characteristic in that it's
performance is substantially invariant with amplitude.
[] Yet another system and method to compute a confidence estimate
that also
can be appropriate is provided as follows. The spectral entropy is computed by
normalizing
the spectrum to take the form of a probability mass function. The Shannon
entropy or
uncertainty function, H, is then computed as follows:
H = -Epi log, pi
where p = probability, and i = frequency index value (e.g. to +40 hz).
[] It is to be appreciated that other approaches to quantify
randomness or
uncertainty of the spectrum may also be suitable for use with the various
DDD_MRS signal
processor aspects of the present disclosure.
[] For further understanding and clarity re: the ultimate impact
frame editing
as described herein, the unprocessed absorption spectrum plot for all six
channels from the
patient (with the compromised frames included as aggregated in the respective
channel
spectra) in various views in prior Figures is shown for each respective
channel in the six
indicated panes shown in FIG. The phase and frequency corrected spectrum
averaged for
selected channels 3 and 4, and for all acquired frames aggregated/averaged
per channel,
without applying frame editing and thus including the corrupted frames, is
shown in FIG.
16A. In contrast, FIG. 16B shows a similar phase and frequency error corrected
spectrum
averaged for the same selected channels 3 and 4, but for only of the
acquired frames
aggregated/averaged per channel (the remaining frames edited out), per
frame editing
applied according to the present embodiments prior to frequency error
correction. The peak


CA
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PCT/US/
value in the combined lactate-alanine (LAAL) region of the frame edited
spectrum of FIG.
16B is significantly increased ¨ with corresponding increase in SNR ¨ relative
to the peak
value in the same LAAL region of the non-frame edited spectrum in FIG. 16A
(e.g. the peak
value increases from about x to about x ), a nearly 20% SNR
increase despite
about 40% corresponding reduction in the number of FID frames used.
[] While the examples addressed above by reference to FIGS. B
address a highly beneficial embodiment for frame editing based upon water
signal, other
frame editing embodiments are also contemplated, and many different features
of acquired
DDD-MRS signals may be used for this purpose. One such further embodiment is
shown for
example by reference to another DDD-MRS pulse sequence acquisition for another
disc in
another subject by reference to FIGS. 17A-F. More specifically, per the time-
intensity plot
shown for this acquisition in FIG. 17A, while the water signal region of the
acquired spectral
series (bright vertical band on left side of plot) reveals some shift
artifact, another bright band
appears at a broader region on the right side of the spectra, between about
and FID
frames into the exam. This region is associated with lipid, and also overlaps
with lactic acid
(LA) and alanine (AL) regions of diagnostic interest according to the present
detailed
embodiments and Examples. This is further reflected in FIG. 17B which shows a
waterfall
plot of running cumulative average of acquired frames in series, where signal
amplitude rises
in this lipid-related spectral region during this portion of the exam. A
resulting average
spectral plot for channels 1 and 2 of this acquisition, post phase and
frequency correction
(again noting water signal did not prompt frame editing to remove many frames)
is shown for
reference in FIG. 17C. This resulting spectrum has significant signal peak
intensity and line
width commensurate with lipid signal, and which shrouds an ability to assess
underlying LA
and/or AL chemicals overlapping therewith in their respective regions.
Accordingly, an
ability to measure LA and AL being compromised may also compromise an ability
to make a
diagnostic assessment of tissue based upon these chemicals (as if un
compromised by
overlapping lipid). However, as this lipid contribution clearly only occurs
mid-scan, an
ability to edit it out to assess signal without that portion of the exam may
provide a robust
result for LA and AL-based evaluation nonetheless.
[] This is shown in FIGS. 17D-F where only the first frames of
the same
acquisition are evaluated, which occur prior to the lipid contribution arising
in the acquired


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signals. As is shown here, no lipid signal is revealed in the time intensity
plot of FIG. 17D,
or waterfall plot of FIG. 17E, or resulting final spectrum of FIG. 17F, though
strong
proteoglycan peak is shown with very little (if any) LA or AL in the signal of
otherwise high
SNR (e.g. per the PG peak). As this example illustrates a DDD-MRS processed
acquisition
for a non-painful control disc, strong PG signal and little to no LA and/or AL
signal is
typically expected, and this thus represents a diagnostically useful, robust
signal for intended
purpose (whereas the prior spectra without editing out the lipid frames may
have erroneously
biased the results). Accordingly, it is contemplated that a lipid editor may
also be employed
as a further embodiment for frame editing, with approaches for recognizing
lipid signal taken

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  • Wen He(Tue Jul 23 - EST)
• RE: [EXT] Re: [v5,2/2] Documentation: dt: binding: rtc: add binding for ftm alarm driver
  • Biwen Li(Wed Jul 17 - EST)
  • Biwen Li(Wed Jul 17 - EST)
• Re: [f2fs-dev] [PATCH] f2fs: fix to do sanity with enabled features in image
  • Chao Yu(Tue Jul 16 - EST)
  • Chao Yu(Tue Jul 23 - EST)
• Re: [f2fs-dev] [PATCH] f2fs: fix to read source block before invalidating it
  • Chao Yu(Wed Jul 17 - EST)
  • Chao Yu(Wed Jul 17 - EST)
• Re: [f2fs-dev] [PATCH v2] f2fs: fix to read source block before invalidating it
  • Chao Yu(Wed Jul 17 - EST)
  • Chao Yu(Wed Jul 17 - EST)
  • Chao Yu(Thu Jul 18 - EST)
  • Chao Yu(Tue Jul 23 - EST)
• {Filename?} Rezygnacja z zamowienia
• Firing an interrupt pin induces the occurrence of another interrupt
• fix nvme performance regression due to dma_max_mapping_size()
• [for-next][PATCH 0/5] tracing: last minute changes before pushing
• Re: [for-next][PATCH 12/16] kprobes: Initialize kprobes at postcore_initcall
• [for-next][PATCH 1/5] ftrace/selftests: Return the skip code when tracing directory not configured in kernel
• [for-next][PATCH 2/5] ftrace/selftest: Test if set_event/ftrace_pid exists before writing
• [for-next][PATCH 3/5] tracing: Pass type into tracing_generic_entry_update()
• [for-next][PATCH 4/5] tracing: Let filter_assign_type() detect FILTER_PTR_STRING
• [for-next][PATCH 5/5] tracing: Make trace_get_fields() global
• Re: [Freedreno] [PATCH] drm/msm: correct NULL pointer dereference in context_init
• Re: [Freedreno] [PATCH] drm/msm: stop abusing dma_map/unmap for cache
• From: africanamericanchildrenbooks.com Muzashah,
• Re: [Fwd: [PATCH 1/2] string: Add stracpy and stracpy_pad mechanisms]
• general protection fault in kstrtouint (2)
  • syzbot(Thu Jul 18 - EST)
• general protection fault in make_kuid
  • syzbot(Mon Jul 22 - EST)
• general protection fault in tcf_ife_init
  • syzbot(Mon Jul 22 - EST)
• general protection fault in tls_setsockopt
  • syzbot(Tue Jul 16 - EST)
• get_africanamericanchildrenbooks.com subsystem output
• [GIT] Crypto Fixes for
• [GIT] Networking
• [GIT PULL 0/4] ARM: SoC contents for merge window
• [GIT PULL 1/4] ARM: SoC platform updates
• [GIT PULL 2/4] ARM: SoC-related driver updates
• [GIT PULL 3/4] ARM: Device-tree updates
• [GIT PULL 4/4] ARM: SoC defconfig updates
• [GIT PULL] Additional ACPI updates for vrc1
• [GIT PULL] arch/h update
• [GIT PULL] arch/sh update
• [GIT PULL] ARC updates for rc1
• Re: [GIT PULL] Backlight for v
• [GIT PULL] Btrfs fixes for rc2
• Re: [GIT PULL] Btrfs updates for
• [GIT PULL] Ceph updates for rc1
• [GIT PULL] clk changes for the merge window
• [GIT pull] core/urgent for rc1
• [GIT PULL] csky changes for vrc1
  • guoren(Fri Jul 19 - EST)
• [GIT PULL] dax for
• [GIT PULL] Devicetree fixes for rc
• [GIT PULL] dmaengine updates for vrc1
• [GIT PULL] dma-mapping fixes for
• [GIT PULL] final round of SCSI updates for the + merge window
  • Ming Lei(Fri Jul 19 - EST)
• [GIT PULL for vrc1] docs: addition of a large set of files to the documentation body
• [GIT PULL for vrc1] media updates
• [GIT PULL] GPIO fixes for v take one
• [git pull] habanalabs fixes for rc2
  • Greg KH(Mon Jul 22 - EST)
• [GIT PULL] hwspinlock updates for v
• [git pull] Input updates for vrc0
• [GIT PULL] iomap: cleanups for (part 2)
• [GIT PULL] libnvdimm for
• Re: [GIT PULL] MFD for v
• [GIT PULL] MIPS changes
• [GIT PULL] Modules updates for v
• [GIT PULL] More Kbuild updates for vrc1
• [GIT PULL] More power management updates for vrc1
• [GIT PULL] NTB changes for v
• [GIT PULL] parisc architecture fixes for kernel vrc1
• [GIT PULL] parisc architecture fixes for kernel vrc2
• Re: [GIT PULL] PCI changes for v
• [GIT PULL] perf/core improvements and fixes
• [GIT PULL] perf/urgent fixes
• [GIT pull] perf/urgent for rc1
• Re: [GIT PULL] pidfd and clone3 fixes
• [GIT PULL] pidfd fixes
• [GIT PULL] platform-drivers-x86 for
• [GIT PULL] Please pull NFS changes for Linux
• Re: [GIT PULL] Please pull RDMA subsystem changes
• Re: [GIT PULL] power-supply changes for
• [GIT PULL] remoteproc updates for v
• [GIT PULL REQUEST] watchdog - v Merge window
• [GIT PULL] RISC-V updates for v
• [GIT PULL] rpmsg updates for v
• [GIT PULL] RTC for
• Re: [GIT PULL] SafeSetID LSM changes for
• [GIT pull] sched/urgent for rc1
• [GIT pull] sched/urgent for rc2
• [GIT PULL] Second batch of KVM changes for merge window
• [GIT PULL] SMB3 Fixes
• [GIT pull] smp/urgent for rc1
• [GIT PULL] sound fixes for rc1
• [GIT PULL] (swiotlb) for-linus
• [GIT PULL] Thermal management updates for vrc1
• [GIT PULL] tracing: Changes for
• [GIT PULL] tracing: Fix user stack trace "??" output
• [git pull] typo fix
  • Al Viro(Sat Jul 20 - EST)
• Re: [GIT PULL v2] updates to soc/fsl drivers for next(v)
• [GIT PULL] VFIO updates for vrc1
• [git pull] africanamericanchildrenbooks.com adfs patches
  • Al Viro(Thu Jul 18 - EST)
• [git pull] africanamericanchildrenbooks.com - dcache and mountpoint stuff
  • Al Viro(Fri Jul 19 - EST)
• [git pull] africanamericanchildrenbooks.com misc
  • Al Viro(Thu Jul 18 - EST)
• [git pull] africanamericanchildrenbooks.com africanamericanchildrenbooks.com0
  • Al Viro(Thu Jul 18 - EST)
• Re: [GIT PULL] Wimplicit-fallthrough patches for rc1
• [GIT PULL] Wimplicit-fallthrough patches for rc2
• [GIT pull] x86/urgent for rc1
• [GIT PULL] xen: fixes and features for rc1
• [GIT PULL] xfs: cleanups for
• gpio: aspeed: Add SGPIO driver
• Re: [gpio] f69e00bd africanamericanchildrenbooks.com % regression
• Re: Greetings.
• Greetings My Dear Friend,
• Greetings my good friend
• Hello
• HELLO
• Hello
  • Jenny(Mon Jul 22 - EST)
• Hello have you see my message i send to you?
• HELLO! PLEASE TRY AND RESPOND SOONEST
• Help: Regression in v : do_IRQ: No irq handler for vector
• HMM_MIRROR has less than useful help text
• hmm_range_fault related fixes and legacy API removal v2
• If interested
  • Tk Allen(Wed Jul 17 - EST)
• Re: incoming
• Re: INFO: rcu detected stall in ext4_write_checks
• INFO: task hung in hwrng_unregister
  • syzbot(Tue Jul 23 - EST)
• INFO: trying to register non-static key in usbtouch_open
  • syzbot(Tue Jul 23 - EST)
• Re: [Intel-gfx] [PATCH] drm/i Mark expected switch fall-throughs
• Re: [Intel-gfx] [PATCH] drm/i Use dev_get_drvdata
• Re: [Intel-gfx] [PATCH v3 1/3] drm/gem: don't force writecombine mmap'ing
• Re: [Intel-gfx] screen freeze with rc6 Dell XPS skylake i
• Re: [Intel-wired-lan] [PATCH 1/2] ee: add workaround for possible stalled packet
• Re: [Intel-wired-lan] [PATCH 2/2] ee: disable force K1-off feature
• RE: [Intel-wired-lan] [PATCH] i40e: reduce stack usage in i40e_set_fc
• Re: io_uring question
• iscsi_ibft: change ISCSI_IBFT depends on ACPI instead of ISCSI_IBFT_FIND
• Is Linux Kernel able to support Intel Core iXE Extreme Edition or AMD Ryzen 9 X?
• Issue with sequence to switch to HS
• Je serai ravis de faire ta connaissance
• Re: KASAN: global-out-of-bounds Read in dvb_pll_attach
  • syzbot(Wed Jul 17 - EST)
  • syzbot(Fri Jul 19 - EST)
• Re: kasan: paging percpu + kasan causes a double fault
• KASAN: slab-out-of-bounds Read in do_jit
  • syzbot(Tue Jul 23 - EST)
• KASAN: slab-out-of-bounds Write in check_noncircular
  • syzbot(Fri Jul 19 - EST)
• KASAN: use-after-free Read in device_release_driver_internal
  • syzbot(Tue Jul 23 - EST)
• KASAN: use-after-free Read in finish_task_switch (2)
  • syzbot(Fri Jul 19 - EST)
  • syzbot(Fri Jul 19 - EST)
• KASAN: use-after-free Read in h5_rx_3wire_hdr
  • syzbot(Mon Jul 22 - EST)
• KASAN: use-after-free Read in hidraw_ioctl
  • syzbot(Tue Jul 23 - EST)
• KASAN: use-after-free Read in nr_insert_socket
  • syzbot(Thu Jul 18 - EST)
• Re: KASAN: use-after-free Read in nr_release
  • syzbot(Wed Jul 17 - EST)
  • syzbot(Thu Jul 18 - EST)
• KASAN: use-after-free Read in nr_rx_frame (2)
  • syzbot(Tue Jul 23 - EST)
• KASAN: use-after-free Read in usbhid_power
  • syzbot(Tue Jul 23 - EST)
• KASAN: use-after-free Write in check_noncircular
  • syzbot(Wed Jul 17 - EST)
• KASAN: use-after-free Write in nr_insert_socket
  • syzbot(Tue Jul 23 - EST)
• KASAN: use-after-free Write in tlb_finish_mmu
  • syzbot(Fri Jul 19 - EST)
  • syzbot(Fri Jul 19 - EST)
• kbuild: Fail if gold linker is detected
• Re: [kbuild:kbuild 5/19] drivers/atm/eni.o: warning: objtool: eni_init_one()+0xe indirect call found in RETPOLINE build
• Kernel and newer have broke my Bluetooth keyboard/mouse support
• kernel BUG at drivers/usb/wusbcore/wusbhc.c:LINE!
  • syzbot(Tue Jul 23 - EST)
• Re: kernel BUG at mm/swap_state.c!
• kernel panic: stack is corrupted in pointer
  • syzbot(Wed Jul 17 - EST)
  • syzbot(Tue Jul 23 - EST)
  • syzbot(Tue Jul 23 - EST)
• Re: leds: apu: drop obsolete support for apu>=2
• Re: [Letux-kernel] [PATCH v3 0/5] drm/panel-simple: Add panel parameters for ortustech-com37h3m05dtc/99dtc and sharp-lqy3dg3b
• lift the xfs writepage code into iomap v3
• Limits for ION Memory Allocator
• Linux
  • Greg KH(Sun Jul 21 - EST)
  • Greg KH(Sun Jul 21 - EST)
• Linux
  • Greg KH(Sun Jul 21 - EST)
  • Greg KH(Sun Jul 21 - EST)
• Linux
  • Greg KH(Sun Jul 21 - EST)
  • Greg KH(Sun Jul 21 - EST)
• Linux
  • Greg KH(Sun Jul 21 - EST)
  • Greg KH(Sun Jul 21 - EST)
• Linux
  • Greg KH(Sun Jul 21 - EST)
  • Greg KH(Sun Jul 21 - EST)
• Linux
  • Greg KH(Sun Jul 21 - EST)
  • Greg KH(Sun Jul 21 - EST)
• RE: linux/drivers/net/wireless/realtek/rtlwifi/rtl8*/sw.c: many redundant assignments ?
  • Pkshih(Mon Jul 22 - EST)
• Linux rc1
• linux-headers and proper use of SIOCGSTAMP
• Re: [Linux-kernel-mentees] [PATCH] PCI: Remove unused EXPORT_SYMBOL()s from drivers/pci/bus.c
  • Greg KH(Wed Jul 17 - EST)
• Re: [Linux-kernel-mentees] [PATCH] PCI: Stop exporting pci_bus_sem
• Re: [Linux-kernel-mentees] [PATCH v3] PCI: Remove functions not called in include/linux/pci.h
• Re: linux-next boot error: WARNING: workqueue cpumask: online intersect > possible intersect
• linux-next: build failure after merge of the extcon tree
• linux-next: build failure after merge of the kbuild tree
• Re: linux-next: build failure after merge of the rdma tree
• linux-next: build failure after merge of the v4l-dvb tree
• linux-next: build warning after merge of the africanamericanchildrenbooks.comt tree
• linux-next: build warning after merge of the cifs tree
• linux-next: build warning after merge of the input-current tree
• Re: linux-next: build warning after merge of the ntb tree
• linux-next: Fixes tag needs some work in the drm tree
• Re: linux-next: Fixes tag needs some work in the hyperv-fixes tree
• linux-next: Fixes tag needs some work in the mmc-fixes tree
• linux-next: Fixes tag needs some work in the net-next tree
• linux-next: Fixes tag needs some work in the sfixes tree
• linux-next: Fixes tags need some work in the sound-asoc-fixes tree
• Re: linux-next: manual merge of the akpm-current tree with the hmm tree
• linux-next: manual merge of the devicetree tree with Linus' tree
• linux-next: manual merge of the drm-intel tree with the kspp-gustavo tree
• linux-next: manual merge of the keys tree with the afs tree
• linux-next: manual merge of the net tree with Linus' tree
• Re: linux-next: manual merge of the rdma tree with the v4l-dvb-next tree
• linux-next: manual merge of the xfs tree with Linus' tree
• [linux-next] mm/i i_gemfs_init() NULL dereference
• linux-next: Signed-off-by missing for commit in the amdgpu tree
• Re: linux-next: Tree for Jul 15 (HEADERS_TEST w/ netfilter tables offload)
• linux-next: Tree for Jul 16
• linux-next: Tree for Jul 17
• linux-next: Tree for Jul 18
• Re: linux-next: Tree for Jul 18 (header build error)
• linux-next: Tree for Jul 19
• linux-next: Tree for Jul 22
• linux-next: Tree for Jul 23
• [LINUX PATCH v18 1/2] mtd: rawnand: nand_micron: Do not over write driver's read_page()/write_page()
• [LINUX PATCH v18 2/2] mtd: rawnand: pl Add basic driver for arm pl smc nand interface
• Re: list corruption in deferred_split_scan()
  • Yang Shi(Tue Jul 16 - EST)
  • Yang Shi(Wed Jul 17 - EST)
  • Qian Cai(Thu Jul 18 - EST)
  • Yang Shi(Thu Jul 18 - EST)
• Re: [LKP] [btrfs] c8eaeac7b7: africanamericanchildrenbooks.com-per-min % regression
• Re: [LKP] [gpio] f69e00bd africanamericanchildrenbooks.com % regression
• [LPC ] Power Management and Thermal Control MC: Call for topics
• Re: May the peace of God be with you,you,
• memory leak in kobject_set_name_vargs (2)
  • syzbot(Tue Jul 23 - EST)
• Re: memory leak in llc_ui_create (2)
  • syzbot(Wed Jul 17 - EST)
• memory leak in new_inode_pseudo (2)
  • syzbot(Tue Jul 16 - EST)
  • syzbot(Tue Jul 16 - EST)
• memory leak in policydb_read
  • syzbot(Tue Jul 23 - EST)
• memory leak in rds_send_probe
  • syzbot(Tue Jul 23 - EST)
  • syzbot(Tue Jul 23 - EST)
• mmotm uploaded
  • akpm(Tue Jul 16 - EST)
• mmotm uploaded
  • akpm(Wed Jul 17 - EST)
• Re: mmotm uploaded (MTD_HYPERBUS, HBMC_AM)
• mmotm uploaded
  • akpm(Thu Jul 18 - EST)
• net boot error: WARNING: workqueue cpumask: online intersect > possible intersect (2)
  • syzbot(Tue Jul 23 - EST)
• Re: [net-next 1/2] ipvs: batch __ip_vs_cleanup
• net-next boot error: WARNING: workqueue cpumask: online intersect > possible intersect (2)
  • syzbot(Tue Jul 23 - EST)
• network problems with r
• next, imx Oops when loading a module
• Re: nl wlcore regression in next
• Re: [Non-DoD Source] Re: [RFC PATCH v2] fanotify, inotify, dnotify, security: add security hook for fs notifications
• [No Subject]
• NULL ptr deref in wq_worker_sleeping on
• Re: objtool crashes on clang output (drivers/hwmon/pmbus/admo)
• OK
  • AHM AHM(Wed Jul 17 - EST)
• Re: [PATCH 00/10] Add further support for PHYTEC phyBOARD-Segin
• [PATCH 00/10] Add support for new SAI IP version
• [PATCH 00/10] cpufreq: Migrate users of policy notifiers to QoS requests
• [PATCH 00/10] Refactor rxe driver to remove multiple race conditions
• Re: [PATCH 00/11] PCI: Move symbols to drivers/pci/pci.h
• [PATCH 00/14] crypto: caam - fixes for kernel v
• [PATCH 00/14] mfd: convert subsystem to i2c_new_dummy_device()
• [PATCH 00/14] PCI/P2PDMA: Support transactions that hit the host bridge
• Re: [PATCH 00/22] x86, objtool: several fixes/improvements
• [PATCH 01/10] ASoC: fsl_sai: add of_match data
• [PATCH 01/10] cpufreq: Add policy create/remove notifiers
• Re: [PATCH 01/10] drm/mxsfb: Update mxsfb to support a bridge
• [PATCH 01/10] perf script: Fix --max-blocks man page description
• [PATCH 01/10] Simplify rxe_run_task interface
• [PATCH 01/12] list.h: add list_pop and list_pop_entry helpers
• Re: [PATCH 01/12 v2] Platform: add a dev_groups pointer to struct platform_driver
• [PATCH 01/14] crypto: caam/qi - fix error handling in ERN handler
• [PATCH 01/14] mfd: 88pm convert to i2c_new_dummy_device
• [PATCH 01/14] PCI/P2PDMA: Add constants for not-supported result upstream_bridge_distance()
• [PATCH 01/14] sched: introduce task_se_h_load helper
• Re: [PATCH 01/18] dt: psci: Update DT bindings to support hierarchical PSCI states
• [PATCH 01/26] drm/dp_mst: Move link address dumping into a function
• [PATCH 01/37] perf include bpf: Add bpf_tail_call() prototype
• [PATCH 01/79] perf tools: Move loaded out of struct perf_counts_values
• [PATCH 0/1] extcon: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/1] gpu: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/1] x86/stacktrace: Fix userstacktrace access_ok() WARNING in irq events
• Re: [PATCH 02/10] ARM: dts: imx6ul: segin: Add boot media to dts filename
• [PATCH 02/10] ASoC: fsl_sai: derive TX FIFO watermark from FIFO depth
• Re: [PATCH 02/10] drm/mxsfb: Update mxsfb with additional pixel formats
• [PATCH 02/10] perf script: Improve man page description of metrics
• [PATCH 02/10] Remove counter that does not have a meaning anymore
• [PATCH 02/10] video: safb: Remove cpufreq policy notifier
• Re: [PATCH 02/11] firmware: arm_scmi: Segregate tx channel handling and prepare to add rx
• Re: [PATCH 02/12] Documentation/arm: repointer docs to Documentation/arch/arm
• [PATCH 02/12] xfs: initialize iomap->flags in xfs_bmbt_to_iomap
• Re: [PATCH 02/13] cpufreq: qcom: Re-organise kryo cpufreq to use it for other nvmem based qcom socs
• [PATCH 02/14] crypto: caam - fix return code in completion callbacks
• [PATCH 02/14] mfd: 88pmx-core: convert to i2c_new_dummy_device
• [PATCH 02/14] PCI/P2PDMA: Factor out __upstream_bridge_distance()
• [PATCH 02/14] sched: change /proc/sched_debug fields
• [PATCH 02/26] drm/dp_mst: Destroy mstbs from destroy_connector_work
• [PATCH 02/37] perf bpf: Do not attach a BPF prog to a tracepoint if its name starts with !
• [PATCH 02/79] perf tools: Rename struct cpu_map to struct perf_cpu_map
• Re: [PATCH 0/2] arm dts: imx8mq: Add DT node for the Mixel MIPI D-PHY
• [PATCH 0/2] arm/arm Add support for function error injection
  • Leo Yan(Tue Jul 16 - EST)
  • Leo Yan(Wed Jul 17 - EST)
• Re: [PATCH 0/2] drm/vkms: Introduce basic support for configfs
• [PATCH 0/2] Fix NULL pointer dereference with virtio-blk-pci and virtual IOMMU
• [PATCH 0/2] hwmon: (k8temp) update to use new hwmon registration API
• [PATCH 0/2] media: ir-kbd-i2c: fix potential OOPS & minor cleanup
• [PATCH 0/2] misc: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/2] mmc: core: Fix Marvell WiFi reset by adding SDIO API to replug card
• [PATCH 0/2] mm/hmm: more HMM clean up
• [PATCH 0/2] nvmem: imx-ocotp: allow reads with arbitrary size and offset
• [PATCH 0/2] panic/printk/x Prevent some more printk-related deadlocks in panic()
• Re: [PATCH 0/2] perf/hw_breakpoint: Fix breakpoint overcommit issue
• [PATCH 0/2] power: Refactor device level sysfs.
• [PATCH 0/2] Redefine interconnect provider DT nodes for SDM
• [PATCH 0/2] Remove blk_mq_sched_started_request function
• [PATCH 0/2] string: Add stracpy and stracpy_pad
• Re: [PATCH 0/2] Support for buttons on newer MS Surface devices
• [PATCH 0/2] Support kexec/kdump for clang built kernel
• [PATCH 0/2] xen/gntdev: sanitize user interface handling
• [PATCH 03/10] ASoC: fsl_sai: Add registers definition for multiple datalines
• [PATCH 03/10] Make pool interface more type safe
• [PATCH 03/10] perf script: Fix off by one in brstackinsn IPC computation
• [PATCH 03/10] video: pxafb: Remove cpufreq policy notifier
• [PATCH 03/12] xfs: use a struct iomap in xfs_writepage_ctx
• [PATCH 03/14] crypto: caam - update IV only when crypto operation succeeds
• [PATCH 03/14] mfd: abcore: convert to i2c_new_dummy_device
• [PATCH 03/14] PCI/P2PDMA: Apply host bridge white list for ACS
• [PATCH 03/14] sched,fair: redefine runnable_load_avg as the sum of task_h_load
• [PATCH 03/26] drm/dp_mst: Move test_calc_pbn_mode() into an actual selftest
• [PATCH 03/37] perf evsel: Store backpointer to attached bpf_object
• [PATCH 03/79] perf tools: Rename struct thread_map to struct perf_thread_map
• [PATCH 0/3] ACPI: Remove unnecessary acpi_has_method() calls
• [PATCH 0/3] Add DSP node on africanamericanchildrenbooks.com8QXP board
• Re: [PATCH 0/3] Add power domain range for MU side b / IRQSTR_DSP
• [PATCH 0/3] Add support for africanamericanchildrenbooks.com AI_ML board
• [PATCH 0/3] arm Allow early timestamping of kernel log
• [PATCH 0/3] arm kprobes: Fix some bugs in arm64 kprobes
• Re: [PATCH 0/3] drm/sun4i: Add support for color encoding and range
• [PATCH 0/3] hwmon: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/3] iio: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/3] introduce __put_user_pages(), convert a few call sites
• [PATCH 0/3] media: Add support for Cadence CSI2TX version
• [PATCH 0/3] mm/hmm: fixes for device private page migration
• Re: [PATCH 0/3] perf: Use capabilities instead of uid and euid
• Re: [PATCH 0/3] resource: find_next_iomem_res() improvements
• [PATCH 0/3] sgi-gru: get_user_page changes
• [PATCH 0/3 v2] Sync unmappings in vmalloc/ioremap areas
• [PATCH 0/3 v3] Sync unmappings in vmalloc/ioremap areas
• [PATCH 0/3][V4] iio: imu: Add support for the ADIS IMU
• [PATCH 04/10] arch_topology: Use CPUFREQ_CREATE_POLICY instead of CPUFREQ_NOTIFY
• [PATCH 04/10] ASoC: fsl_sai: Update Tx/Rx channel enable mask
• [PATCH 04/10] perf tools: Fix proper buffer size for feature processing
• Re: [PATCH 04/10] Protect kref_put with the lock
• [PATCH 04/12] xfs: refactor the ioend merging code
• Re: [PATCH 04/13] cpufreq: qcom: Refactor the driver to make it easier to extend
• [PATCH 04/14] crypto: caam - check key length
• [PATCH 04/14] mfd: bcmxx: convert to i2c_new_dummy_device
• [PATCH 04/14] PCI/P2PDMA: Cache the result of upstream_bridge_distance()
• [PATCH 04/14] sched,fair: move runnable_load_avg to cfs_rq
• [PATCH 04/26] drm/print: Add drm_err_printer()
• [PATCH 04/37] perf trace: Add pointer to BPF object containing __augmented_syscalls__
• [PATCH 04/79] perf tools: Rename struct perf_evsel to struct evsel
• [PATCH 0/4] Arm MHUv2 Mailbox Controller Driver
• [PATCH 0/4] clk: meson: ao: use the new parent description method
• [PATCH 0/4] Lockdep: Reduce stack trace memory usage
• [PATCH 0/4] new driver for TI eQEP
• [PATCH 0/4] put_user_page: new put_user_page_dirty*() helpers
• [PATCH 0/4] rtc: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/4] Sleeping functions in invalid context bug fixes
• [PATCH 0/4][V2] iio: imu: Add support for the ADIS IMU
• [PATCH 0/4][V3] iio: imu: Add support for the ADIS IMU
• [PATCH 0/4] Various rwsem ACQUIRE fixes
• [PATCH 0/4] video: of: display_timing: Adjust err printing of of_get_display_timing()
• [PATCH 05/10] ASoC: fsl_sai: Add support to enable multiple data lines
• Re: [PATCH 05/10] dt-bindings: display: Add max-res property for mxsfb
• Re: [PATCH 05/10] Fix reference counting for rxe tasklets
• [PATCH 05/10] perf stat: Fix segfault for event group in repeat mode
• [PATCH 05/10] thermal: cpu_cooling: Switch to QoS requests instead of cpufreq notifier
• Re: [PATCH 05/11] firmware: arm_scmi: Add receive buffer support for notifications
• [PATCH 05/12] xfs: turn io_append_trans into an io_private void pointer
• [PATCH 05/14] crypto: caam - check authsize
• [PATCH 05/14] mfd: dacore: convert to i2c_new_dummy_device
• [PATCH 05/14] PCI/P2PDMA: Factor out host_bridge_whitelist()
• [PATCH 05/26] drm/dp_mst: Add sideband down request tracing + selftests
• [PATCH 05/37] perf trace: Look up maps just on the __augmented_syscalls__ BPF object
• [PATCH 05/79] perf tools: Rename struct perf_evlist to struct evlist
• Re: [PATCH 0/5] Add Bitmain BM clock driver
• Re: [PATCH 0/5]Add support for mt JPEG ENC support
  • mtk(Wed Jul 17 - EST)
• [PATCH 0/5] Add support for WD MyCloud EX2 Ultra (+ versatile UART-based restart/poweroff drivers)
• [PATCH 0/5] arm dts: qcom: sdm Fix DTS warnings
• [PATCH 0/5] mtd: spi-nor: write protection at power-up
• [PATCH 0/5] sched/fair: rework the CFS load balance
• [PATCH 06/10] ASoC: dt-bindings: Document dl_mask property
• [PATCH 06/10] perf stat: Always separate stalled cycles per insn
• [PATCH 06/10] powerpc: macintosh: Switch to QoS requests instead of cpufreq notifier
• [PATCH 06/10] Remove pd form rxe_ah
• Re: [PATCH 06/12] dt-bindings: clock: qcom: Add reset for WCSSAON
• Re: [PATCH 06/12] mmc: meson-mx-sdio: Fix misuse of GENMASK macro
• [PATCH 06/12] xfs: remove the fork fields in the writepage_ctx and ioend
• [PATCH 06/14] crypto: caam - check assoclen
• [PATCH 06/14] mfd: max convert to i2c_new_dummy_device
• [PATCH 06/14] PCI/P2PDMA: Add whitelist support for Intel Host Bridges
• [PATCH 06/14] sched,cfs: use explicit cfs_rq of parent se helper
• [PATCH 06/26] drm/dp_mst: Move PDT teardown for ports into destroy_connector_work
• [PATCH 06/37] perf trace: Order -e syscalls table
• [PATCH 06/79] perf tools: Rename perf_evsel__init to evsel__init
• [PATCH 0/6] ARM: psci: cpuidle: PSCI CPUidle rework
• [PATCH 0/6] Fixes for meta data acceleration
• [PATCH 07/10] ASoC: fsl_sai: Add support for FIFO combine mode
• [PATCH 07/10] cpufreq: powerpc_cbe: Switch to QoS requests instead of cpufreq notifier
• Re: [PATCH 07/10] Pass the return value of kref_put further
• [PATCH 07/10] perf session: Fix loading of compressed data split across adjacent records
• Re: [PATCH 07/11] firmware: arm_scmi: Add support for asynchronous commands and delayed response
• Re: [PATCH 07/12] clk: qcom: Add WCSSAON reset
• [PATCH 07/12] iomap: move the xfs writeback code to iomap.c
• [PATCH 07/14] crypto: caam - check zero-length input
• [PATCH 07/14] mfd: max convert to i2c_new_dummy_device
• [PATCH 07/14] PCI/P2PDMA: Add the provider's pci_dev to the dev_pgmap struct
• [PATCH 07/14] sched,cfs: fix zero length timeslice calculation
• Re: [PATCH 07/18] drivers: firmware: psci: Prepare to use OS initiated suspend mode
• Re: [PATCH 07/22] x86/uaccess: Remove ELF function annotation from copy_user_handle_tail()
• [PATCH 07/26] drm/dp_mst: Get rid of list clear in drm_dp_finish_destroy_port()
• [PATCH 07/37] perf trace: Add BPF handler for unaugmented syscalls
• [PATCH 07/79] perf tools: Rename perf_evlist__init to evlist__init
• [PATCH 0/7] renesas, cmt: DT Binding Documentation and Minor Driver Updates
• [PATCH 08/10] ACPI: cpufreq: Switch to QoS requests instead of cpufreq notifier
• [PATCH 08/10] ASoC: dt-bindings: Document fcomb_mode property
• [PATCH 08/10] Move responsibility of cleaning up pool elements
• [PATCH 08/10] perf probe: Set pev->nargs to zero after freeing pev->args entries
• [PATCH 08/12] iomap: add tracing for the address space operations
• [PATCH 08/14] crypto: caam - update rfc sh desc to support zero length input
• [PATCH 08/14] mfd: max convert to i2c_new_dummy_device
• [PATCH 08/14] PCI/P2PDMA: Add attrs argument to pci_p2pdma_map_sg()
• [PATCH 08/14] sched,fair: simplify timeslice length code
• [PATCH 08/26] drm/dp_mst: Refactor drm_dp_send_enum_path_resources
• [PATCH 08/37] perf trace: Allow specifying the bpf prog to augment specific syscalls
• [PATCH 08/79] perf tools: Rename perf_evlist__new to evlist__new
• [PATCH 0/8] Backported fixes for stable tree
• [PATCH 0/8] clk: meson: ee: use the new parent description method
• [PATCH 0/8] Introduce generic ONFI support
• [PATCH 0/8] media: convert subsystem to i2c_new_dummy_device()
• [PATCH 0/8] PM / ACPI: sleep: Simplify the suspend-to-idle control flow
• [PATCH 0/8] ti-sysc related warning fixes for vrc cycle
• [PATCH 09/10] ASoC: fsl_sai: Add support for SAI new version
• [PATCH 09/10] Consolidate resetting of QP's tasks into one place
• [PATCH 09/10] cpufreq: Remove CPUFREQ_ADJUST and CPUFREQ_NOTIFY policy notifier events
• [PATCH 09/10] perf probe: Avoid calling freeing routine multiple times for same pointer
• [PATCH 09/12] iomap: move struct iomap_page out of iomap.h
• [PATCH 09/14] crypto: caam - keep both virtual and dma key addresses
• [PATCH 09/14] mfd: max convert to i2c_new_dummy_device
• [PATCH 09/14] PCI/P2PDMA: Introduce pci_p2pdma_unmap_sg()
• [PATCH 09/14] sched,fair: refactor enqueue/dequeue_entity
• Re: [PATCH 09/16] sparc use the generic get_user_pages_fast code
• Re: [PATCH 09/18] drivers: firmware: psci: Add support for PM domains using genpd
• [PATCH 09/26] drm/dp_mst: Remove huge conditional in drm_dp_mst_handle_up_req()
• [PATCH 09/37] perf trace: Put the per-syscall entry/exit prog_array BPF map infrastructure in place
• [PATCH 09/79] perf tools: Rename perf_evlist__delete to evlist__delete
• Re: [PATCH 0/9] Harden list_for_each_entry_rcu() and family
• [PATCH 0/9] irqchip/gic-v3: Add support for GICv extended PPI/SPI ranges
• [PATCH 10/10] ASoC: fsl_sai: Add support for imx7ulp/imx8mq
• [PATCH 10/10] Documentation: cpufreq: Update policy notifier documentation
• [PATCH 10/10] perf build: Do not use -Wshadow on gcc <
• Re: [PATCH 10/10] Replace tasklets with workqueues
• Re: [PATCH 10/11] firmware: arm_scmi: Drop config flag in clk_ops->rate_set
• [PATCH 10/12] iomap: warn on inline maps in iomap_writepage_map
• Re: [PATCH 10/12] phy: amlogic: G12A: Fix misuse of GENMASK macro
• [PATCH 10/14] crypto: caam - fix DKP for certain key lengths
• [PATCH 10/14] mfd: maxi2c: convert to i2c_new_dummy_device
• [PATCH 10/14] PCI/P2PDMA: Factor out __pci_p2pdma_map_sg()
• [PATCH 10/14] sched,fair: add helper functions for flattened runqueue
• Re: [PATCH 10/18] drivers: firmware: psci: Add hierarchical domain idle states converter
• Re: [PATCH 10/22] bpf: Disable GCC -fgcse optimization for ___bpf_prog_run()
• [PATCH 10/26] drm/dp_mst: Constify guid in drm_dp_get_mst_branch_by_guid()
• [PATCH 10/37] perf trace: Handle raw_syscalls:sys_enter just like the BPF_OUTPUT augmented event
• [PATCH 10/79] perf tools: Rename perf_evsel__delete to evsel__delete
• Re: [PATCH 11/11] firmware: arm_scmi: Use asynchronous CLOCK_RATE_SET when possible
• [PATCH 11/12] xfs: set IOMAP_F_NEW more carefully
• Re: [PATCH 11/13] arm dts: qcom: qcs Add CPR and populate OPP table
• [PATCH 11/14] crypto: caam - free resources in case caam_rng registration failed
• [PATCH 11/14] mfd: max convert to i2c_new_dummy_device
• [PATCH 11/14] PCI/P2PDMA: dma_map P2PDMA map requests that traverse the host bridge
• [PATCH 11/14] sched,fair: flatten hierarchical runqueues
• [PATCH 11/26] drm/dp_mst: Refactor drm_dp_mst_handle_up_req()
• [PATCH 11/37] perf augmented_raw_syscalls: Add handler for "openat"
• [PATCH 11/79] perf tools: Rename perf_evsel__new to evsel__new
• [PATCH 1/1] extcon: extcon-max convert to i2c_new_dummy_device
• [PATCH 1/1] gpu: drm: bridge: analogix-anx78xx: convert to i2c_new_dummy_device
• [PATCH 1/1] iommu/vt-d: Correctly check format of page table in debugfs
  • Lu Baolu(Fri Jul 19 - EST)
• Re: [patch 1/1] Kconfig: Introduce CONFIG_PREEMPT_RT
• [PATCH 1/1] mm/memory_hotplug: Adds option to hot-add memory in ZONE_MOVABLE
• [PATCH 1/1] x86/stacktrace: Fix userstacktrace access_ok() WARNING in irq events
• Re: [PATCH 12/12] arm dts: qcom: Enable Q6v5 WCSS for ipq SoC
  • gokulsri(Thu Jul 18 - EST)
• Re: [PATCH 12/12] closures: fix a race on wakeup from closure_sync
  • Coly Li(Tue Jul 16 - EST)
  • Coly Li(Thu Jul 18 - EST)
• [PATCH 12/12] iomap: zero newly allocated mapped blocks
• [PATCH 12/14] crypto: caam - execute module exit point only if necessary
• [PATCH 12/14] mfd: max convert to i2c_new_dummy_device
• [PATCH 12/14] PCI/P2PDMA: No longer require no-mmu for host bridge whitelist
• [PATCH 12/14] sched,fair: track cfs_rq->max_h_load for more legitimate h_weight
• [PATCH 12/26] drm/dp_mst: Refactor drm_dp_mst_handle_down_rep()
• [PATCH 12/37] perf augmented_raw_syscalls: Switch to using BPF_MAP_TYPE_PROG_ARRAY
• [PATCH 12/79] perf tools: Rename perf_evlist__add to evlist__add
• Re: [PATCH 1/2] arch: mark syscall number reserved for clone3
• [PATCH 1/2] ARCv2: IDU-intc: Add support for edge-triggered interrupts
• [PATCH 1/2] arm Add support for function error injection
  • Leo Yan(Tue Jul 16 - EST)
• Re: [PATCH 1/2] arm dts: imx8mq: Add gpio-ranges property
• [PATCH 1/2] arm dts: lsa: Fix GPIO work fail.
  • Hui Song(Thu Jul 18 - EST)
• Re: [PATCH 1/2] arm mm: Restore mm_cpumask (revert commit 38da ("arm mm: kill mm_cpumask usage"))
• Re: [PATCH 1/2] ARM: dts: aspeed: tiogapass: Add VR devices
• Re: [PATCH 1/2] ARM: dts: imx6ul: Add Variscite DART-6UL SoM support
• Re: [PATCH 1/2] ASoC: samsung: odroid: fix an use-after-free issue for codec
• [PATCH 1/2] block: blk-mq: Remove blk_mq_sched_started_request function
• [PATCH 1/2] block: drbd: Fix a possible null-pointer dereference in receive_protocol()
• [PATCH 1/2] char: hw_random: imx-rngc: use devm_platform_ioremap_resource() to simplify code
• [PATCH 1/2] checkpatch: Don't interpret stack dumps as commit IDs
• Re: [PATCH 1/2] clk: imx8mm: rename lcdif pixel clock
• [PATCH 1/2] crypto: ccree - check assoclen for rfc
• [PATCH 1/2] dma-mapping: add a dma_addressing_limited helper
• [PATCH 1/2] dma-mapping: Protect dma_addressing_limited against NULL dma_mask
• [PATCH 1/2] doc:it_IT: align translation to mainline
• Re: [PATCH 1/2] drivers: qcom: rpmh-rsc: simplify TCS locking
• [PATCH 1/2] drm/gem: don't force writecombine mmap'ing
• Re: [PATCH 1/2] drm/ttm: use the same attributes when freeing d_page->vaddr
• Re: [PATCH 1/2] dt-bindings: i2c: Add MediaTek i2c AC timing binding
  • Qii Wang(Wed Jul 17 - EST)
• [PATCH 1/2] dt-bindings: iio: avia-hx Fix avdd-supply typo in example
• [PATCH 1/2] dt-bindings: interconnect: Update Qualcomm SDM DT bindings
• [PATCH 1/2] dt/bindings: mips: Document Ingenic SoCs binding
• [PATCH 1/2] f2fs: fix to spread f2fs_is_checkpoint_ready()
  • Chao Yu(Mon Jul 22 - EST)
• Re: [PATCH 1/2] f2fs: include charset encoding information in the superblock
• [PATCH 1/2] habanalabs: fix F/W download in BE architecture
• [PATCH 1/2] HID: core: Add hid printk_once macros
• [PATCH 1/2] hwmon: (k8temp) update to use new hwmon registration API
• [PATCH 1/2] i2c: mxs: use devm_platform_ioremap_resource() to simplify code
• [PATCH 1/2] i avoid calling lockdep_assert_irqs_disabled on PREEMPT_RT_FULL
• [PATCH 1/2] iommu/vt-d: Don't queue_iova() if there is no flush queue
  • Lu Baolu(Fri Jul 19 - EST)
• [PATCH 1/2] kbuild: test headers listed in header-test-m as well
• [PATCH 1/2] kernel/fork: Add support for stack-end guard page
• [PATCH 1/2] KVM: Boost vCPUs that are delivering interrupts
• [PATCH 1/2] KVM: X Fix fpu state crash in kvm guest
• [PATCH 1/2] livepatch: Nullify obj->mod in klp_module_coming()'s error path
• [PATCH 1/2] media: ir-kbd-i2c: prevent potential NULL pointer access
• Re: [PATCH 1/2] MIPS: Rename JZRISC to XBURST
• [PATCH 1/2] misc: eeprom: ee convert to i2c_new_dummy_device
• [PATCH 1/2] mmc: core: Add sdio_trigger_replug() API
• [PATCH 1/2] mmc: sdhci: Add PLL Enable support to internal clock setup
• [PATCH 1/2] mm/filemap: don't initiate writeback if mapping has no dirty pages
• [PATCH 1/2] mm/hmm: a few more C style and comment clean ups
• [PATCH 1/2] mm/memcontrol: fix flushing per-cpu counters in memcg_hotplug_cpu_dead
• Re: [PATCH 1/2] mm,sparse: Fix deactivate_section for early sections
• Re: [PATCH 1/2] net/macb: bindings doc: add sifive fuc binding
• [PATCH 1/2] nvme-core: Fix extra device_put() call on error path
• [PATCH 1/2] nvmem: imx-ocotp: use constant for write restriction
• [PATCH 1/2] opp: Not all power-domains are scalable
• [PATCH 1/2] [PATCH] gpio: Replace usage of bare 'unsigned' with 'unsigned int'
• [PATCH 1/2] printk/panic: Access the main printk log in panic() only when safe
• [PATCH 1/2] [RESEND] dmaengine: omap-dma: make omap_dma_filter_fn private
• Re: [PATCH 1/2] soc: imx8: Add africanamericanchildrenbooks.com8MQ UID(unique identifier) support
• Re: [PATCH 1/2] staging: rts Rewrite redundant if statement to improve code style
• [PATCH 1/2] string: Add stracpy and stracpy_pad mechanisms
• Re: [PATCH 1/2] tracing: replace simple_strtol() by kstrtoint()
• [PATCH 1/2] usb: host: oxuhp-hcd: remove include/linux/oxuhp.h
• [PATCH 1/2 V2] power: sysfs: Remove wakeup_abort_count attribute.
  • Greg KH(Tue Jul 23 - EST)
• [PATCH 1/2] virtio-mmio: Process vrings more proactively
  • Fei Li(Fri Jul 19 - EST)
• [PATCH 1/2] x86/purgatory: add -mno-sse, -mno-mmx, -mno-sse2 to Makefile
  • Greg KH(Wed Jul 17 - EST)
• [PATCH 1/2] xen/gntdev: replace global limit of mapped pages by limit per call
• [PATCH 13/14] crypto: caam - unregister algorithm only if the registration succeeded
• [PATCH 13/14] mfd: palmas: convert to i2c_new_dummy_device
• [PATCH 13/14] PCI/P2PDMA: Update documentation for pci_p2pdma_distance_many()
• [PATCH 13/14] sched,fair: flatten update_curr functionality
• [PATCH 13/26] drm/dp_mst: Destroy topology_mgr mutexes
• [PATCH 13/37] perf augmented_raw_syscalls: Support copying two string syscall args
• [PATCH 13/79] perf tools: Rename perf_evlist__remove to evlist__remove
• [PATCH 1/3] ACPI: Remove acpi_has_method() call from acpi_adxl.c
• [PATCH 1/3] arm kprobes: Recover pstate.D in single-step exception handler
• [PATCH 1/3] ARM: dts: vfzii-spb4: Drop unused pinctrl_i2c1 pinmux config
• Re: [PATCH 1/3] bindings: rtc: add bindings for MT RTC
• [PATCH 1/3] cifs: Add support for root file systems
• [PATCH 1/3] clk: imx8: Add DSP related clocks
• [PATCH 1/3] drivers/gpu/drm/via: convert put_page() to put_user_page*()
• [PATCH 1/3] dt-bindings: Add Vendor prefix for Einfochips
• [PATCH 1/3] dt-bindings: Document JZ47xx VPU auxiliary processor
• Re: [PATCH 1/3] dt-bindings: media: i2c: Add IMX CMOS sensor binding
• RE: [PATCH 1/3] firmware: imx: scu-pid: Rename mu PD range to mu_a
• [PATCH 1/3] hwmon: asb convert to i2c_new_dummy_device
• [PATCH 1/3] iio: light: cm convert to i2c_new_dummy_device
• [PATCH 1/3] kbuild: use $(basename ) for cmd_asn1_compiler
• [PATCH 1/3] macb: bindings doc: update sifive fuc binding
• [PATCH 1/3] media: dt-bindings: Update bindings for Cadence CSI2TX version
• [PATCH 1/3] mm: document zone device struct page reserved fields
• [PATCH 1/3] mm/gup: introduce __put_user_pages()
• [PATCH 1/3] mt fix checkpatch warnings and errors
• [PATCH 1/3] [net-next] net: remove netx ethernet driver
• Re: [PATCH 1/3] nvme: Pass the queue to SQ_SIZE/CQ_SIZE macros
• Re: [PATCH 1/3] perf: Add capability-related utilities
• Re: [PATCH 1/3] perf script: Fix --max-blocks man page description
• Re: [PATCH 1/3] platform/x86/pcengines-apuv2: add mpcie reset gpio export
• [PATCH 1/3] power: supply: ab remove set but not used variables 'vbup33_vrtcn' and 'bup_vch_range'
• [PATCH 1/3] printk: Allow architecture-specific timestamping function
• [PATCH 1/3] scsi: qla2xxx: Replace vmalloc + memset with vzalloc
• [PATCH 1/3] sgi-gru: Convert put_page() to get_user_page*()
• [PATCH 1/3] soc: qcom: llcc cleanup to get rid of sdm specific driver file
• RE: [PATCH 1/3] usb: common: Add usb_get_dr_mode_from_string and usb_dr_mode_to_string.
• Re: [PATCH 1/3] usb: host: xhci-plat: Add support for Exynos5/DWC3 specific variant
• [PATCH 1/3] [v2] dmaengine: dw-edma: fix unnecessary stack usage
• [PATCH 1/3][V4] iio: imu: adis: Add support for SPI transfer cs_change_delay
• [PATCH 1/3] x86/mm: Check for pfn instead of page in vmalloc_sync_one()
• [PATCH 14/14] crypto: caam - change return value in case CAAM has no MDHA
• [PATCH 14/14] mfd: twl-core: convert to i2c_new_dummy_device
• [PATCH 14/14] PCI/P2PDMA: Introduce pci_p2pdma_[un]map_resource()
• [PATCH 14/14] sched,fair: propagate sum_exec_runtime up the hierarchy
• Re: [PATCH 14/18] drivers: firmware: psci: Manage runtime PM in the idle path for CPUs
• [PATCH 14/26] drm/dp_mst: Cleanup drm_dp_send_link_address() a bit
• [PATCH 14/37] perf trace: Look for default name for entries in the syscalls prog array
• [PATCH 14/79] perf tools: Rename perf_evsel__open to evsel__open
• Re: [PATCH 1/4] arm Enable TIMER_IMX_SYS_CTR for ARCH_MXC platforms
• [PATCH 1/4] ARM: dts: imx6sx: move GIC to right location in DT
• Re: [PATCH 1/4] block: elevator.c: Remove now unused elevator= argument
  • Bob Liu(Mon Jul 22 - EST)
• [PATCH 1/4] clk: meson: g12a-aoclk: migrate to the new parent description method
• [PATCH 1/4] dt-bindings: counter: new bindings for TI eQEP
• Re: [PATCH 1/4] dt-bindings: thermal: imx8mm-thermal: Add binding doc for africanamericanchildrenbooks.com8MM
• [PATCH 1/4] libceph: allow ceph_buffer_put() to receive a NULL ceph_buffer
• [PATCH 1/4] locking/lockdep: Make it clear that what lock_class::key points at is not modified
• [PATCH 1/4] locking/rwsem: Add missing ACQUIRE to read_slowpath exit when queue is empty
• [PATCH 1/4] mailbox: arm_mhuv2: add device tree binding documentation
• Re: [PATCH 1/4] numa: introduce per-cgroup numa balancing locality, statistic
• [PATCH 1/4] rtc: isl convert to i2c_new_dummy_device
• [PATCH 1/4][V2] drivers: spi: core: Add optional delay between cs_change transfers
• [PATCH 1/4][V3] spi: Add optional stall delay between cs_change transfers
• [PATCH 1/4] video: of: display_timing: Add of_node_put() in of_get_display_timing()
• [PATCH 15/26] drm/dp_mst: Refactor pdt setup/teardown, add more locking
• [PATCH 15/37] perf augmented_raw_syscalls: Rename augmented_args_filename to augmented_args_payload
• [PATCH 15/79] perf tools: Rename perf_evsel__enable to evsel__enable
• [PATCH 1/5] arm dts: qcom: sdm Add unit name to soc node
• Re: [PATCH 1/5] Input: fsl-imxtcq: Use devm_platform_ioremap_resource()
• [PATCH 1/5] media: dt-bindings: Add JPEG ENC device tree node document
• Re: [PATCH 1/5] MIPS: Disallow CPU_SUPPORTS_HUGEPAGES for XPA,EVA
• Re: [PATCH 1/5] MIPS: ralink: add dt binding header for mtpll
• [PATCH 1/5] mtd: spi-nor: fix description for int (*flash_is_locked)()
• [PATCH 1/5] power: reset: Add UART-based MCU poweroff DT bindings
• [PATCH 1/5] sched/fair: clean up asym packing
• [PATCH 1/5] x86_ -march=native support
• [PATCH 16/26] drm/dp_mst: Rename drm_dp_add_port and drm_dp_update_port
• [PATCH 16/37] perf script: Fix --max-blocks man page description
• [PATCH 16/79] perf tools: Rename perf_evsel__disable to evsel__disable
• [PATCH 1/6] ARM: cpuidle: Remove useless header include
• Re: [PATCH 1/6] ARM: ks watchdog: stop using mach/*.h
• Re: [PATCH 1/6] clk: Sync prototypes for clk_bulk_enable()
• Re: [PATCH 1/6] dt-bindings: irqchip: Add PRUSS interrupt controller bindings
• Re: [PATCH 1/6] KVM: x Add callback functions for handling APIC ID, DFR and LDR update
• Re: [PATCH 1/6] leds: apu: drop superseeded apu2/3 led support
• [PATCH 1/6] mm: always return EBUSY for invalid ranges in hmm_range_{fault,snapshot}
• [PATCH 1/6] vhost: don't set uaddr for invalid address
• Re: [PATCH 17/18] arm dts: Convert to the hierarchical CPU topology layout for MSM
• [PATCH 17/26] drm/dp_mst: Remove lies in {up,down}_rep_recv documentation
• [PATCH 17/37] perf script: Improve man page description of metrics
• [PATCH 17/79] perf tools: Rename perf_evsel__apply_filter to evsel__apply_filter
• [PATCH 1/7] dt-bindings: timer: renesas, cmt: Add CMT to sh73a0 and r8a
• [PATCH 1/7] lib/string: add strnchrnul()
• Re: [PATCH 18/18] arm dts: hikey: Convert to the hierarchical CPU topology layout
• [PATCH 18/26] drm/dp_mst: Handle UP requests asynchronously
• [PATCH 18/37] perf script: Fix off by one in brstackinsn IPC computation
• [PATCH 18/79] perf tools: Rename perf_evsel__cpus to evsel__cpus
• [PATCH 1/8] ARM: OMAP2+: Fix missing SYSC_HAS_RESET_STATUS for dra7 epwmss
• [PATCH 1/8] clk: meson: g12a: move clock declaration to dependency order
• [PATCH 1/8] media: dvb-frontends: cxdr_core: convert to i2c_new_dummy_device
• [PATCH 1/8] mm: make page ref count overflow check tighter and more explicit
• [PATCH 1/8] mtd: nand: move ONFI related functions to onfi.h
• [PATCH 1/8] PCI: irq: Introduce rearm_wake_irq()
• Re: [PATCH 1/8] tpm: block messages while suspended
• [PATCH 19/26] drm/dp_mst: Protect drm_dp_mst_port members with connection_mutex
• [PATCH 19/37] perf tools: Fix proper buffer size for feature processing
• [PATCH 19/79] perf tools: Rename perf_evlist__open to evlist__open
• [PATCH 1/9] irqchip/gic: Rework gic_configure_irq to take the full ICFGR base
• Re: [PATCH 20/22] objtool: Fix seg fault on bad switch table entry
• [PATCH 20/26] drm/dp_mst: Don't forget to update port->input in drm_dp_mst_handle_conn_stat()
• [PATCH 20/37] perf stat: Fix segfault for event group in repeat mode
• [PATCH 20/79] perf tools: Rename perf_evlist__close to evlist__close
• [PATCH 21/26] drm/nouveau: Don't grab runtime PM refs for HPD IRQs
• [PATCH 21/37] perf augmented_raw_syscalls: Augment sockaddr arg in 'connect'
• [PATCH 21/79] perf tools: Rename perf_evlist__enable to evlist__enable
• [PATCH 22/26] drm/amdgpu: Iterate through DRM connectors correctly
• [PATCH 22/37] perf trace beauty: Make connect's addrlen be printed as an int, not hex
• [PATCH 22/79] perf tools: Rename perf_evlist__disable to evlist__disable
• [PATCH 2/2] arm dts: sdm Redefine interconnect provider DT nodes
• Re: [PATCH 2/2] arm tlb: Add boot parameter to disable TLB flush within the same inner shareable domain
• [PATCH 2/2] arm: Add support for function error injection
  • Leo Yan(Tue Jul 16 - EST)
• Re: [PATCH 2/2] ARM: dts: Add support for africanamericanchildrenbooks.com6 UltraLite DART Variscite Customboard
• Re: [PATCH 2/2] ARM: dts: aspeed: tiogapass: Add Riser card
• Re: [PATCH 2/2] ASoC: samsung: odroid: fix a double-free issue for cpu_dai
• [PATCH 2/2] block: drbd: Fix a possible null-pointer dereference in is_valid_state()
• [PATCH 2/2] char: hw_random: mxc-rnga: use devm_platform_ioremap_resource() to simplify code
• [PATCH 2/2] checkpatch: Improve SPDX license checking
• Re: [PATCH 2/2] clk: imx8mm: rename 'share_count_dcss' to 'share_count_disp'
• Re: [PATCH 2/2] clk: meson: axg-audio: add g12a reset support
• [PATCH 2/2] crypto: bcm - check assoclen for rfc/rfc
• [PATCH 2/2] dma-direct: only limit the mapping size if swiotlb could be used
• [PATCH 2/2] doc:it_IT: rephrase statement
• Re: [PATCH 2/2] drivers: qcom: rpmh-rsc: fix read back of trigger register
• [PATCH 2/2] drm/vgem: use normal cached mmap'ings
• [PATCH 2/2] dt-bindings: IDU-intc: Add support for edge-triggered interrupts
• [PATCH 2/2] dt-bindings: iio: ad Fix dtc warnings in example

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» Ubuntu » Packages » focal » libs

CA
MR SPECTROSCOPY SYSTEM AND METHOD FOR DIAGNOSING PAINFUL
AND NON-PAINFUL INTERVERTEBRAL DISCS
BACKGROUND
Field
[] This disclosure relates to systems, processors, devices, and
methods for
measuring chemical constituents in tissue for diagnosing medical conditions.
More
specifically, it relates to systems, pulse sequences, signal and diagnostic
processors,
diagnostic displays, and related methods using novel application of nuclear
magnetic
resonance, including magnetic resonance spectroscopy, for diagnosing pain such
as low back
pain associated with degenerative disc disease.
Description of the Related Art
[] While significant effort has been directed toward improving
treatments for
discogenic back pain, relatively little has been done to improve the diagnosis
of painful discs.
[] Magnetic resonance imaging (MRI) is the primary standard of
diagnostic
care for back pain. An estimated ten million MRIs are done each year for
spine, which is the
single largest category of all MRIs at an estimated 26% of all MRIs performed.
MRI in the
context of back pain is sensitive to changes parted magic 2019_01_03 Free Activators disc and endplate hydration
and structural
morphology, and often yields clinically relevant diagnoses such as in setting
of
spondlyolesthesis and disc herniations with nerve root impingement (e.g.
sciatica). In
particular context of axial back pain, MRI is principally useful for
indicating degree of disc
degeneration. However, parted magic 2019_01_03 Free Activators, degree disc degeneration has not been well correlated
to pain. In
one regard, people free of back pain often have disc degeneration profiles
similar to those of
people with chronic, severe axial back pain. In general, not all degenerative
discs are painful,
and not all painful discs are degenerative. Accordingly, the structural
information provided
by standard MRI exams of the lumbar spine is not generally useful for
differentiating
between painful and non-painful degenerative discs in the region as related to
chronic, severe
back pain.
[] Accordingly, a second line diagnostic exam called "provocative
discography" (PD) is often performed after MRI exams in order to localize
painful discs.
This approach uses a needle injection of pressurized dye in awake patients in
order to
intentionally provoke pain. The patient's subjective reporting of pain level
experienced


CA
during the injection, on increasing scale ofand concordancy to usual
sensation of pain,
is the primary diagnostic data used to determine diagnosis as a "positive
discogram" ¨
indicating painful disc ¨ versus a "negative discogram" for a disc indicating
it is not a source
of the patient's chronic, parted magic 2019_01_03 Free Activators, severe back pain. This has significant limitations
including
invasiveness, pain, risks of disc damage, subjectivity, lack of
standardization of technique, parted magic 2019_01_03 Free Activators.
PD has been particularly challenged for high "false+" rates alleged in various
studies,
although recent developments in the technique and studies related thereto have
alleged
improved specificity of above 90%. (Wolfer et al., Pain Physician ;
, ISSN
). However, the significant patient morbidity of the needle-based
invasive
procedure is non-trivial, as the procedure itself causes severe pain and
further compromises
time from work. Furthermore, in another recent study PD was shown to cause
significant
adverse effects to long term disc health, including significantly accelerating
disc
degeneration and herniation rates (on the lateral side of needle puncture).
(Carragee et al.,
SPINE Volume 34, Number 21, pp, parted magic 2019_01_03 Free Activators.). Controversies around PD
remain,
and in many regards are only growing, despite the on-going parted magic 2019_01_03 Free Activators of the
invasive,
painful, subjective, harmful approach as the secondary standard of care
following MRI. PD parted magic 2019_01_03 Free Activators performed an estimatedtimes annually world-wide, at an estimated
total economic
cost that exceeds $ Million Dollars annually. The need for a non-invasive,
painless,
objective, non-significant risk, more efficient and cost-effective test to
locate painful
intervertebral discs of chronic, severe low back pain patients is urgent and
growing.
[] A non-invasive radiographic technique to accurately differentiate
between
discs that are painful and non-painful may offer significant guidance in
directing treatments
and developing an evidence-based approach to the care of patients with lumbar
degenerative
disc disease (DDD).
SUMMARY
[] One aspect of the present disclosure is a MRS pulse sequence
configured
to generate and acquire a diagnostically useful MRS spectrum from a voxel
located
principally within an intervertebral disc of a patient.
[] Another aspect of the present disclosure is an MRS signal
processor that is
configured to select a sub-set of multiple channel acquisitions received
contemporaneously


CA
WO / PCT/US/
from multiple parallel acquisition channels, respectively, of a multi-channel
detector
assembly during a repetitive-frame MRS pulse sequence series conducted on a
region of
interest within a body of a subject.
[] Another aspect of the present disclosure is an MRS signal
processor
comprising a phase shift corrector configured to recognize and correct phase
shifting within a
repetitive multi-frame acquisition series acquired by a multi-channel detector
assembly
during an MRS pulse sequence series conducted on a region of interest within a
body of a
subject.
[] Another aspect of the present disclosure is a MRS signal
processor
comprising a frequency shift corrector configured to recognize and correct
frequency shifting
between multiple acquisition frames of a repetitive multi-frame acquisition
series acquired
within an acquisition detector channel of a multi-channel detector assembly
during a MRS
pulse sequence series conducted on a region of interest within a body of a
subject.
[] Another aspect of the present disclosure is a MRS signal
processor
comprising a frame editor configured to recognize at least parted magic 2019_01_03 Free Activators poor quality
acquisition frame,
as determined against at least one threshold criterion, within an acquisition
channel of a
repetitive multi-frame acquisition series received from a multi-channel
detector assembly
during a MRS pulse sequence series conducted on a region of interest within a
body of a
subject.
[] Another aspect of the present disclosure is an MRS signal
processor that
comprises an apodizer to reduce the truncation effect on the sample data. The
apodizer can
be configured to apodize an MRS acquisition frame in the time domain otherwise
generated
and acquired by via an MRS aspect otherwise herein disclosed, and/or signal
processed by
one or more of the various MRS signal processor aspects also otherwise herein
disclosed.
[] Another aspect of the present disclosure is an MRS diagnostic
processor
configured to process information extracted from an MRS spectrum for a region
of interest in
a body of a subject, and to provide the processed information in a manner that
is useful for
diagnosing a medical condition or chemical environment associated with the
region of
interest.
[] Another aspect of the present disclosure is an MRS system
comprising an
MRS pulse sequence, MRS signal processor, and MRS diagnostic processor, and
which is


configured to generate, acquire, and process an MRS spectrum representative of
a region of
interest in a body of a patient for providing diagnostically useful
information associated with
the region of interest.
[]
Still further aspects of the present disclosure comprise various MRS
method aspects associated with the other MRS system, sequence, and processor
aspects
described above.
[a] In another embodiment, there is provided a magnetic resonance
spectroscopy (MRS) processing system configured to process a repetitive frame
MRS
spectral acquisition series generated and acquired for a voxel located within
an intervertebral
disc via an MRS pulse sequence, and acquired at multiple acquisition channels
of a multi-coil
spine detector assembly, in order to provide a processed MRS spectrum with at
least one
chemical region from which spectral data may be extracted and processed to
provide
diagnostic information for a medical condition or chemical environment in the
disc. The
MRS processing system includes parted magic 2019_01_03 Free Activators automated MRS signal processor including a
channel
selector, a phase shift corrector, a frequency shift corrector, parted magic 2019_01_03 Free Activators, a frame
editor, and a channel
combiner, and configured to receive and process the MRS spectral acquisition
series for the
disc and to generate at least in part the processed MRS spectrum for the
series. The MRS
signal processor includes at least one of (a) at least one computer processor
and (b) software
provided in computer readable non-transitory storage and that is configured to
be run by at
least one computer processor.
[b] In another embodiment, there is provided a magnetic resonance
spectroscopy (MRS) processing method for processing a repetitive frame MRS
spectral
acquisition series generated and acquired for a voxel located within an
intervertebral disc via
an MRS pulse sequence, and acquired at multiple acquisition channels of a
multi-coil spine
detector assembly, and for providing a processed MRS spectrum for the series
with at least
one chemical region from which spectral data may be extracted to provide MRS-
based
diagnostic information for a medical condition or chemical environment in the
disc. The
method involves receiving the MRS spectral acquisition series from the
multiple acquisition
channels and signal processing the MRS acquisition series, involving selecting
one or more
channels among the channels based upon comparing a measured feature of
acquired data
from a channel against at least one threshold channel selection criterion,
recognizing and

CA

correcting phase shift error among the acquired or partially processed spectra
corresponding
respectively with multiple frames within the series for the one or more
selected channels,
recognizing parted magic 2019_01_03 Free Activators correcting a frequency shift error among the acquired or
partially processed
spectra corresponding respectively with multiple frames within the series of
the one or more
selected channels, recognizing and editing out a first set of excluded frames
and thereby
selecting and retaining a remaining second set of retained frames respectively
from the series
for the one or more selected channels based upon at least one threshold frame
editing
criterion, and combining the retained and phase and frequency shift corrected
frames of the
one or more selected channels for a combined average to provide at least in
part the
processed MRS spectrum. The signal processing is performed by at least one
computer
processor.
[c] In another embodiment, there is provided a magnetic resonance
spectroscopy (MRS) processing system configured to process a repetitive frame
MRS
spectral acquisition series generated and acquired for a voxel principally
located within a
region of interest (ROI) including at least a portion of an intervertebral
disc via an MRS
pulse sequence, and acquired at multiple parallel acquisition channels of a
multi-coil spine
detector assembly, in order to generate a processed MRS spectrum for the ROI
with
identifiable chemical peak regions from which data may be extracted to provide
MRS-based
diagnostic information for diagnosing a medical condition associated with, or
chemical
environment within, the disc. The MRS processing system includes an automated
MRS
signal processor including a frequency shift corrector configured to recognize
and correct a
frequency shift error between multiple frames within the series and a frame
editor configured
to recognize and edit out frames parted magic 2019_01_03 Free Activators the series based upon a predetermined
criteria, and that parted magic 2019_01_03 Free Activators configured to receive and automatically process the acquired MRS spectral
acquisition
series for the disc and to generate the processed MRS spectrum in a frame
edited and
frequency shift corrected form. The MRS signal processor includes at least one
of (a) at least
one computer processor and (b) software provided in computer readable non-
transitory
storage and that is configured to be run by at least one computer processor.
[d] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) processing method for using the system described
above for
processing a repetitive frame MRS spectral acquisition series generated and
acquired for a
-4a-
CA

voxel principally located within a region of interest (ROT) including at least
a portion of an
intervertebral disc via an MRS pulse sequence, and acquired at multiple
parallel acquisition
channels of a multi-coil spine detector assembly, and for generating a
processed MRS
spectrum from the series for the ROT with identifiable chemical peak regions
from which
data may be extracted for providing MRS-based diagnostic information for
diagnosing a
medical condition associated with, or chemical environment within, the disc.
The method
involves receiving the MRS spectral acquisition series from the multiple
acquisition channels
and using the automated MRS signal processor for signal parted magic 2019_01_03 Free Activators the
repetitive frame
MRS spectral acquisition series in an automated manner, and including using
the frequency
shift corrector to recognize and correct a frequency shift error between
multiple frames
within the series, using the frame editor to recognize and edit out frames
from the series
based upon a predetermined criteria, and generating at least in part the
processed MRS
spectrum in a frame edited and frequency corrected form. The signal processing
is performed
by at least one computer processor.
[e] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) method for generating and processing a multi-
frame MRS
spectral acquisition series of data for a voxel located within a region of
interest (ROI) in a
patient to thereby provide a processed MRS spectrum from which spectral data
may be
extracted and processed to provide MRS-based diagnostic information for a
medical
condition associated with the ROT. The method involves: applying a first MRS
pulse
sequence to produce a set of unsuppressed water free induction decay (FID)
frames acquired
using multiple acquisition channels of a multi-coil detector assembly;
applying a second
MRS pulse sequence to produce a set of suppressed water FID frames acquired
using the
multiple acquisition channels of the multi-coil detector assembly; and signal
processing the
MRS spectral acquisition series of data. The signal processing is performed by
at least one
computer processor. The signal processing involves selecting one or more
channels among
the multiple acquisition channels for further processing to generate the
processed spectrum.
The selection of the one or more channels is based at least in part on the set
of unsuppressed
water FID frames for each of the channels. The signal processing further
involves identifying
phase shift error using the set of unsuppressed water FID frames, and applying
phase shift
correction to the set of suppressed water FID frames. The phase shift
correction is configured
-4b-
CA

to at least partially correct the phase shift error determined using the set
of unsuppressed
water FID frames. The signal processing further involves combining at least
some of the
phase shift corrected frames from the set of suppressed water FID frames from
the one or
more selected channels to at least in part produce the processed MRS spectrum.
[f] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) system configured to generate and process a multi-
frame
MRS spectral acquisition series of data for a voxel within a region of
interest (ROT) in a
patient to provide a processed MRS spectrum from which spectral data may be
extracted and
processed to provide diagnostic information for a medical condition associated
with the ROT.
The system includes an MR system including an MR scanner and a multi-coil
detector
assembly, and that is configured to use at least one MRS pulse sequence to non-
invasively
generate and acquire the MRS spectral acquisition series of data from the ROT
using multiple
acquisition channels of the multi-coil detector assembly. The MRS spectral
acquisition series
of data includes a set of unsuppressed water free induction decay (FID) frames
and a set of
suppressed water FID frames. The system further includes an automated MRS
signal
processor configured to receive and process the MRS spectral acquisition
series of data to
generate the processed MRS spectrum. The automated MRS signal processor
includes at
least one of: (a) a channel selector configured to select one or more channels
among the
multiple acquisition channels for further processing to generate the processed
spectrum,
wherein the channel selector is configured to select the one or more channels
based at least in
part on the set of unsuppressed water FID frames for each of the channels; and
(b) a phase
shift corrector configured to identify phase shift error using the set of
unsuppressed water
FID frames and to apply phase shift correction to the set of suppressed water
FID frames,
wherein the phase shift correction is configured to at least partially correct
the phase shift
error determined using the set of unsuppressed water FID frames. The automated parted magic 2019_01_03 Free Activators signal
processor includes a frame combiner configured to combine at least some frames parted magic 2019_01_03 Free Activators the set
of suppressed water FID frames to at least in part produce the processed MRS
spectrum. The
automated MRS signal processor includes at least one of: (a) at parted magic 2019_01_03 Free Activators one
computer processor;
and (b) software provided in computer readable non-transitory storage and that
is configured
to be run by at least one computer processor.
-4c-
CA

[g] In accordance with another embodiment, there is provided a magnetic
resonance spectroscopy (MRS) system configured to process a multi-frame MRS
spectral
acquisition series of data generated and acquired from a voxel within a region
of interest
(ROT) in a patient via an MRS pulse sequence operation of an MRS system, and
to provide a
processed MRS spectrum from which spectral data may be extracted and processed
to
provide diagnostic information for a medical condition associated with the
ROT. The system
includes an automated MRS signal processor configured to receive the MRS
spectral
acquisition series of data that includes a first set of free induction decay
(FID) frames and a
second set of FID frames. The second set of FID frames has more water
suppression than the
first set of FID frames. The automated MRS signal processor is further
configured to perform
one or more signal processing operations based at least in part on the first
set of FID frames.
The one or more signal processing operations modify the second set of FID
frames. The
automated MRS signal processor is further configured to combine at least some
frames from
the second set of FID frames to at least in part produce the processed MRS
spectrum. The
automated MRS signal processor includes at least one of: (a) at least one
computer processor;
and (b) software provided in computer readable non-transitory storage and that
is configured
to be run by at least one computer processor.
[] Each of the foregoing aspects, modes, embodiments, variations, and
features noted above, and those noted elsewhere herein, is considered to
represent
independent value for beneficial use, including even if only for the purpose
of providing as
available for further combination with parted magic 2019_01_03 Free Activators, and whereas their parted magic 2019_01_03 Free Activators
combinations and
sub-combinations as may be made by one of ordinary skill based upon a thorough
review of
this disclosure in its entirety are further contemplated aspects also of
independent value for
beneficial use.
-4d-
CA

BRIEF DESCRIPTION OF THE DRAWINGS
[] These and other features, aspects, and advantages of the
present disclosure
will now be described with reference to the drawings of embodiments, which
embodiments
are intended to illustrate and not to limit the disclosure.
[] FIGS. 1A-C show respective MRI images of an intervertebral disc
region
of a lumbar spine with overlay features representing a voxel prescription
within a disc for
performing a DDD-MRS exam according to one aspect of the disclosure, in
coronal, sagittal, parted magic 2019_01_03 Free Activators,
and axial imaging planes, respectively.
[] FIG. 2 shows an example of the sectional deployment in one
commercially
available MR spine detector coil assembly, and with which certain aspects of
the present
disclosure may be configured to interface for cooperative operation and use,
and have been
so configured and used according to certain Examples provided elsewhere
herein.
[] FIG. 3A shows an example of a CHESS water suppression pulse
sequence
diagram representing certain pulse sequence aspects contemplated by certain
aspects of the
present disclosure.
[] FIG. parted magic 2019_01_03 Free Activators shows certain aspects of a combined CHESS - PRESS pulse

sequence diagram also consistent with certain aspects of the present
disclosure.
-4e-
CA

CA
WO / PCT/US/
[] FIG. 3C shows various different aspects of a combined CHESS ¨ VSS-

PRESS pulse sequence diagram also illustrative of certain aspects of the
present disclosure.
[ FIGS. 4A-B show two examples of respective planar views of a very

selective saturation (VS S) prescription for a voxelated acquisition series in
an intervertebral
disc to be conducted via a DDD-MRS pulse sequence according to further aspects
herein.
[] FIG. 5 shows Real (Sx) and imaginary (Sy) parts of an FID (right)
that
correspond to x and y components of the rotating magnetic moment M (left).
[] FIG. 6 shows an amplitude plot of complex data from a standard
MRS
series acquisition of multiple frame repetitions typically acquired according
to certain present
embodiments, and shows amplitude of signal on the y-axis and time on the x-
axis.
[] FIG, parted magic 2019_01_03 Free Activators. 7 shows a graphical plot of an MRS absorption spectrum from
an
MRS pulse sequence acquisition from a lumbar disc using a 3T MR system, and
which is
produced from the transform of the complex data as the output average after
combining all of
6 activated acquisition channels and averaging all frames, such as typically
provided in
display by a commercially available MRS system, and is generated without
applying the
various signal processing approaches of the present disclosure.
[] FIG. 8 shows a graphical display of individual channel MRS
spectra of all
uncorrected channels of the same MRS acquisition featured in FIG. 7, parted magic 2019_01_03 Free Activators, and is
shown as "real
part squared" representation of the acquired MRS spectral data prior to
combining the
channels, and is also prior to pre-processing according to the signal
processing approaches of
the present disclosure.
[] FIG. 9A shows a schematic flow diagram of one DDD-MRS processor
configuration and processing flow therein, first operating in DDD-MRS signal
processor
mode by conducting optimal channel (coil) selection, phase correcting, then
apodizing, then
transforming domain (from time to frequency), then frame editing (editing out
poor quality
frames while retaining higher quality), then frequency error correction
(correcting for
frequency shifts), then averaging of all selected coils, and then followed by
a DDD-MRS
diagnostic processor and processing flow that comprises data extraction
related to MRS
spectral regions of diagnostic interest, then applying the diagnostic
algorithm, then
generating a diagnostic patient report.


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[] FIG. 9B shows a schematic flow diagram of further detail of
various
component parts of the DDD-MRS signal processor and respective steps taken
thereby as
shown more generally in FIG. 9A.
[] FIG. 9C shows a schematic flow diagram of further detail of
various
component parts of the DDD-MRS diagnostic processor and processing flow taken
thereby
as also shown more generally in FIG. 9A.
[] FIG, parted magic 2019_01_03 Free Activators. 10 shows a plot of phase angle pre- and post- phase
correction for an
acquisition series example, and as is similarly applied for a DDD-MRS
acquisition such as
for a disc according to certain aspects of the present disclosure.
[] FIG. 11 shows the serial acquisition frame averages for each of 6

individual acquisition channels as shown in FIG. 8, but after phase correction
consistent with
the signal processing flow shown in FIGS. 9A-B and phase-correction approach
illustrated in
FIG.
[] FIG. 12 shows the frame-averaged real part squared MRS spectrum
after
combining the strongest two channels (channels 1 and 2) selected among the 6
phase-
corrected frame-averaged channel spectra shown in FIG. 11 using a channel
selection
approach and criterion according to a further aspect of the current
disclosure, but without
frequency correction.
[] FIG. 13 shows an example of a time-intensity plot for a DDD-MRS
acquisition similar to that shown in FIG. 17D for the acquisition shown in
FIGS. and
12, except that the plot of FIG. 13 relates to another MRS pulse sequence
acquisition series
of another lumbar disc in another subject with corrupted frames midway along
the temporal
acquisition series in order to illustrate frame editing according to other
aspects of the
disclosure.
[] FIG. 14A shows confidence in frequency error estimate vs. MRS
frames
temporally acquired across an acquisition series for a disc, as plotted for
the DDD-MRS
series acquisition shown in different view in FIG.
[] FIG. 14B shows a frame by frame frequency error estimate of the
acquisition series featured in FIG. 14A.
[] FIG. 15 shows all 6 frame-averaged acquisition channels for the
series
acquisition conducted on the disc featured in FIGS. B, prior to
correction.


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[] FIG. 16A shows phase corrected, frequency corrected, but not
frame
edited spectral average combining all of acquired series frames for channels 3
and 4 as
combined after optimal channel selection, for the same series acquisition parted magic 2019_01_03 Free Activators in FIGS.

[] FIG. 16B shows phase corrected, frequency corrected, and frame
edited
spectral average combining the partial retained frames not edited out from the
acquired series
for channels 3 and 4 as combined after optimal channel selection, also for the
same series
acquisition featured in FIGS.
[] FIG. 17A parted magic 2019_01_03 Free Activators a 2-dimensional time-intensity plot similar to
that shown
in FIG. 13, but for yet another DDD-MRS acquisition series of another disc in
another
subject and to illustrate another mode of frame editing aspects of the present
disclosure.
[] FIG. 17B shows a waterfall plot in 3-dimensions for the DDD-MRS
acquisition series shown in FIG. 17A, and shows the chemical shift spectrum as
a running
cumulative average at discrete points over time of serial frames acquired,
with spectral
amplitude on the vertical axis.
[] FIG. 17C shows an average DDD-MRS spectrum across the full
acquisition series shown in FIGS. 17A-B, parted magic 2019_01_03 Free Activators, without frame editing, and plots both
phase only
and phase + frequency corrected formats of the spectrum.
[] FIG. 17D shows a 2-dimensional time-intensity plot similar to
that shown
and for the same DDD-MRS acquisition series of FIG. 17A, but only reflecting
retained
frames after editing out other frames according to the present aspect of the
disclosure and
referenced to FIGS. 17A-C.
[] FIG. 17E shows a similar waterfall plot of cumulative spectral
averages
and for the same DDD-MRS acquisition series shown in FIG. 17B, but according
to only the
retained frames after frame editing as shown in FIG. 17D.
[] FIG. 17F shows a similar average DDD-MRS spectrum and for the
same
acquisition series shown in FIG, parted magic 2019_01_03 Free Activators. 17C, but only for the retained frames after
frame editing as
shown in various modes in FIGS. 17D-E.
[] FIGS. 18A-B show time-intensity plots of the same MRS series
acquisition for the same disc featured in FIGS. and as pre- (FIG.
18A) and post-
(FIG. 18B) frequency correction according to a further aspect of the present
disclosure, and


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shows each acquisition frame as a horizontal line along a horizontal frequency
range with
brightness indicating signal amplitude (bright white indicating higher
amplitude, darker
indicating lower), and shows the series of related repetitive frames in
temporal relationship
stacked from top to bottom, e.g. top is time zero).
[] FIGS. 19A-B show the same respective time-intensity plots shown
in FIG.
19A (pre-) and FIG. 19B (post-) frequency correction, but in enhanced contrast
format.
[] FIG. 20 shows spectral plots for 6 frame-averaged acquisition
channels for
the same acquisition shown in FIGS, parted magic 2019_01_03 Free Activators. andexcept post phase and
frequency
correction and prior to optimal channel selection and/or combination channel
averaging.
[] FIG. 21 shows a spectral plot for phase and frequency error
corrected
channels 1 and 2 selected from FIG. 20 as averaged, according to a further
aspect of the
disclosure.
[] FIG. 22 shows a bar graph of mean values, with standard deviation
error
bars, of Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) pain
scores
calculated for certain of the pain patients and asymptomatic volunteers
evaluated in a clinical
study of Example 1 and conducted using certain physical embodiments of a
diagnostic
system constructed according to various aspects of the present disclosure.
[] FIG. 23 shows a Receiver Operator Characteristic (ROC) curve
representing the diagnostic results of the DDD-MRS diagnostic system used in
the clinical
study of Example 1 with human subjects featured in part in FIG. 22, as
compared against
standard control diagnostic measures for presumed true diagnostic results for
painful vs. non-
painful discs.
[] FIG. 24 shows a partition analysis plot for cross-correlation of
a portion of
the clinical diagnostic results of the DDD-MRS system under the same clinical
study of
Example 1 and also addressed in FIGS.based on partitioning of the data
at various
limits attributed to different weighted factors used in the DDD-MRS diagnostic
processor,
with "x" data point plots for negative control discs and "o" data point plots
for positive
control discs, also shows certain statistical results including correlation
coefficient ( R2).
[] FIG. 25A shows a scatter plot histogram of DDD-MRS diagnostic
results
for each disc evaluated in the clinical study of Example 1 and also addressed
in FIGS.
and shows the DDD-MRS results separately for positive control (PC) discs
(positive on


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provocative discography or "PD+"), parted magic 2019_01_03 Free Activators, negative control (NC) discs (negative on
provocative
discography or "PD-", plus discs from asymptomatic volunteers or "ASY"), PD-
alone, and
ASY alone.
[] FIG. 25B shows a bar graph of the same DDD-MRS diagnostic results

shown in FIG. 25A across the same subject groups of Example 1, but shows the
mean values
with standard deviation error bars for the data.
[] FIG. 26 shows a bar graph of presumed true and false binary
"positive"
and "negative" diagnostic results produced by the DDD-MRS system for painful
and non-
painful disc diagnoses in the clinical study of Example 1, as compared against
standard
control diagnostic measures across the positive controls, negative controls
(including sub-
groups), and all discs evaluated in total in the study.
[] FIG. 27 shows diagnostic performance measures of Sensitivity,
Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV),
and area
under the curve (AUC) which in this case is equivalent to Global Performance
Accuracy
(GPA) for the DDD-MRS diagnostic results in the clinical study of Example 1.
[] FIG. 28 shows a bar graph comparing areas under the curve (AUC)
per
ROC analysis of MRI alone (for prostate cancer diagnosis), MRI+ PROSE (MRS
package
for prostate cancer diagnosis), MRI alone (for discogenic back pain or DDD
pain), and MRI
+ DDD-MRS (for discogenic back pain or DDD pain), with bold arrows showing
relative
impact of PROSE vs. DDD-MRS on AUC vs. MM alone for the respective different
applications and indications, with DDD-MRS results shown as provided under
Example 1.
[] FIG. 29 shows positive predictive value (PPV) and negative
predictive
value (NPV) for MRI alone and for MRI + DDD-MRS (per Example 1 results), both
as
applied for diagnosing DDD pain, vs. standard control measures such as
provocative
discography.
[] FIG. 30A shows a plot of DDD-MRS algorithm output data for a
series of
8 L4-L5 lumbar discs in 8 asymptomatic human control subjects per clinically
acquired and
processed DDD-MRS exam under Example 1, and plots these results twice for each
disc on
first (1) and second (2) separate repeat scan dates in order to demonstrate
repeatability of the
DDD-MRS exam's diagnostic results.


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[] FIG. 30B shows a plot of PG/LAAL ratio data for 3 discs per DDD-
MRS
pulse sequence and signal processing data of Example 1, and shows the
clinically acquired
results via 3T DDD-MRS exams of the discs in vivo in pain patients (y-axis)
against acquired
measurements for the same chemicals in the same disc material but flash frozen
after surgical
removal and using 11T HR-MAS spectroscopy.
[] FIG. 31A shows a digitized post-processed Parted magic 2019_01_03 Free Activators spectrum (in
phase
real power) as processed according to certain of the MRS signal processor
aspects of the
present disclosure, and certain calculated data derived therefrom as developed
and used for
calculated signal-to-noise ratio (SNR) of the processed result, as taken
across a sub-set of
samples evaluated under Example 1.
[] FIG. 31B shows a digitized pre-processed DDD-MRS spectrum
(absorption) as 6 channel spectral average without deploying the MRS signal
processing
aspects of the present disclosure (e.g. "pre-processing"), and certain
calculated data derived
therefrom as developed and used for calculated signal-to-noise ratio (SNR) of
the processed
result.
[] FIG. 31C shows a scatter plot histogram of signal-to-noise ratio
(SNR) for
standard "all channels, non-corrected" frame averaged MRS spectra (absorption)
produced
by the 3T MR system for a subset of discs evaluated using the DDD-MRS pulse
sequence in
the clinical study of Example 1, and the SNR of MRS spectra (in phase real
power) for the
same series acquisitions for the same discs post-processed by the DDD-MRS
signal
processor configured according to various of the present aspects of this
disclosure, parted magic 2019_01_03 Free Activators, as such
SNR data was derived for example as illustrated in FIGS. 31A-B.
[] FIG. 31D shows the same data shown in FIG. 31C, parted magic 2019_01_03 Free Activators, but as bar graph
showing mean values and standard deviation error bars for the data within each
pre-
processed and post-processed groups.
[] FIG. 31E shows a scatter plot histogram of the ratio of SNR
values
calculated post- versus pre- processing for each of the discs per the SNR data
shown in FIGS.
31C -D .
[] FIG. 31F shows a bar graph of mean value and standard deviation
error
bar of the absolute difference between post- and pre-processed SNR values for
each of the
discs shown in different views in FIGS. 31C-E.


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[] FIGS. 31G-H respectively show the mean and standard deviation for

absolute improvement between pre- and post- processed SNR (FIG. 31F), the mean
ratio
improvement of post-processed/pre-processed SNR (FIG. 31G), and the mean %
improvement of post-processed vs. pre-processed SNR (FIG. 31H).
[] FIG. 32A shows a mid-sagittal T2-weighted MRI image of a patient
evaluated under the clinical study of Example 1 and comparing the diagnostic
results of the
operating embodiment for DDD-MRS system developed according to various aspects
herein
against provocative discography results for the same discs, and shows a number-
coded (and
also may be color coded) diagnostic legend for the DDD-MRS results (on left of
image) and
discogram legend (top right on image) with overlay of the DDD-MRS results and
discogram
results on discs evaluated in the patient.
[] FIG. 32B shows a mid-sagittal T2-weighted MRI image of another
patient
evaluated under the clinical study of Example 1 and comparing the diagnostic
results of the
physical embodiment DDD-MRS system developed according to various aspects
herein
against provocative discography results for the same discs, and shows a number-
coded (and
also may be color coded) diagnostic legend for the DDD-MRS results (on left of
image) and
discogram legend (top right on image) with overlay of the DDD-MRS results and
discogram
results on discs evaluated in the patient.
[] FIG. 33A shows a scatter plot histogram plot of DDD-MRS (or
"Nociscan") diagnostic results against control groups for various discs
evaluated in vivo
according to the data all my movies crack reviewed and processed under Example 2, as similarly
shown for the
data evaluated in Example 1 in FIG. 25A (plus the further addition of certain
additional
information further provided as overlay to the graph and related to another
aspect of data
analysis applied according to further aspects of the present disclosure under
Example 2).
[] FIG. 33B shows another scatter plot histogram of another
processed form
of the DDD-MRS diagnostic results also shown in FIG. 33A and per Example 2, parted magic 2019_01_03 Free Activators,
after
transformation of the DDD-MRS diagnostic algorithm results for the discs into
"%
probability painful" assigned to each disc as distributed across the positive
(POS) and
negative (NEG) control group sub-populations shown.
[] FIG. 34A shows a scatter plot histogram of signal-to-noise ratio
(SNR) for
standard "all channels, non-corrected" frame averaged MRS spectra (absorption)
produced


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by the 3T MR system for a subset of discs evaluated using the DDD-MRS pulse
sequence
and signal processor in the clinical study of Example 2, and the SNR of
spectra (absorption)
for the same series acquisitions for the same discs post-processed by the DDD-
MRS
processor, as such SNR data was derived for example as illustrated in FIGS, parted magic 2019_01_03 Free Activators.
31A-B.
FIG. 34B shows the same data shown in FIG. 34A, but as bar graph
showing mean values and standard deviation error bars for the data within each
pre-
processed and post-processed groups.
[] FIG, parted magic 2019_01_03 Free Activators. 34C shows a scatter plot histogram of the ratio of SNR
values
calculated post- versus pre-processing for each discs per the SNR data shown
in FIGS. 34A-
B.
[] FIG. 34D shows a bar graph of mean value and standard deviation
error
bar of the absolute difference between post- and pre-processed SNR values for
each of the
discs shown in different views in FIGS. 34A-C.
[] FIG. 34E shows a bar graph of mean value and standard deviation
error
bar of the ratio of post- to pre-processed SNR values for each of the discs
shown in different
views in FIGS. 34A-D.
[] FIG. 34F shows a bar graph of parted magic 2019_01_03 Free Activators mean value and standard
deviation
error bar for the percent increase in SNR from pre- to post-processed MRS
spectra for each
of the discs further featured in FIGS. 34A-E.
[] FIG. 35 shows a DDD-MRS spectrum illustrative of a perceived
potential
lipid signal contribution as overlaps with the regions otherwise also
associated with lactic
acid or lactate (LA) and alanine (AL), according to further aspects of the
present disclosure
and as relates to Example 3.
[] FIG. 36 shows a scatter plot histogram of DDD-MRS diagnostic
algorithm
results for the test population of in vivo discs, as calculated for a defined
Group A evaluated
for diagnostic purposes via Formula A, under the Example 3.
[] FIGS. 37A-C show scatter plot histogram of certain embodiments
for the
DDD-MRS diagnostic processor for discs designated as Group B under Example 3,
including
as shown with respect to PG/LAAL ratio results for the discs (FIG. 17A),
logistic regression
generated Formula B results for the discs (FIG. 17B), and the transformed %
probability pain
distribution for the same Group B discs as a result of the results in FIG. 17B
(FIG. 17C).


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[] FIG. 38 shows a scatter plot histogram of certain embodiments for
DDD-
MRS diagnostic processor for discs designated as Group C discs under Example
3, after
applying logistic regression generated Formula C to the DDD-MRS spectral data
acquired for
the group of discs.
[] FIG. 39 shows a scatter plot histogram of another embodiment for
DDD-
MRS diagnostic algorithm, as applied to Group C discs under Example 3
according to a
Formula B "hybrid" illustrative of yet a further embodiment of the present
disclosure.
[] FIGS. 40A-B show MRI images of two lumbar spine phantoms
according
to another Example 4 of the disclosure.
[] FIGS. 40C-D show graphical plots of n-acetyl (NAA) and lactic
acid (LA)
concentrations in discs from phantoms shown in FIGS. 40A-B as measured
according to
certain DDD-MRS aspects of the present disclosure, versus known amounts, per
Example 4.
[] FIGS. 41A-B show schematic flow diagrams of a DDD-MRS exam,
including DDD-MRS pulse sequence, parted magic 2019_01_03 Free Activators, DDD-MRS signal processing, and DDD-MRS
algorithm processing, and various data communication aspects, parted magic 2019_01_03 Free Activators, according to
certain further
aspects of the present disclosure.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[] Previously reported lab experiments used 11T HR-MAS Spectroscopy
to
compare chemical signatures of different types of ex vivo disc nuclei removed
at surgery.
(Keshari et al., SPINE ) These studies demonstrated that certain chemicals
in disc
nuclei, e.g. lactic acid (LA) and proteoglycan (PG), may provide
spectroscopically
quantifiable metabolic markers for discogenic back pain. This is consistent
with other
studies that suggest DDD pain is associated with poor disc nutrition,
anaerobic metabolism,
lactic acid production (e.g. rising acidity), extracellular matrix degradation
(e.g. reducing
proteoglycan), and increased enervation in the painful disc nuclei. In many
clinical contexts,
ischemia and lowered pH cause pain, likely by provoking acid-sensing ion
channels in
nociceptor sensory neurons.
[] The previous disclosures evaluating surgically removed disc
samples ex
vivo with magnetic resonance spectroscopy (MRS) in a laboratory setting is
quite
encouraging for providing useful diagnostic tool based on MRS. However, an
urgent need
remains for a reliable system and approach for acquiring MRS signatures of the
chemical


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composition of the intervertebral discs in vivo in a readily adoptable
clinical environment,
and to provide a useful, clinically relevant diagnostic tool based on these
acquired MRS
signatures for accurately diagnosing discogenic back pain. A significant need
would be met
by replacing PD with an alternative that, even if diagnostically equivalent,
overcomes one or
more of the significant shortcomings of the PD procedure by being non-
invasive, objective,
pain-free, risk-free, and/or more cost-effective. Magnetic resonance
spectroscopy (MRS) is a
medical diagnostic platform that has been previously developed and
characterized for a
number of applications in medicine. Some of these have been approved such as
for example
for brain tumors, breast cancer, and prostate cancer. Some MRS platforms
disclosed have
been multi-voxel, and others single voxel. None of these have been adequately
configured or
developed for in vivo clinical application to reliably diagnose medical
conditions or chemical
environments associated with nociceptive pain, and/or with respect to
intervertebral discs
such as may be associated with disc degeneration and/or discogenic back pain
(including in
particular, but without limitation, with respect to the lumbar spine).
[] Various technical approaches have also been alleged to enhance
the
quality of MRS acquisitions for certain purposes. However, these approaches
are not
considered generally sufficient to provide the desired spectra of robust,
reliable utility for
many intervertebral discs in vivo, at least not at field strengths typically
employed for in vivo
spectroscopy, e.g. from about tesla (T) or about T to about T or
even up to about
7T. Furthermore, while individual techniques have been disclosed for certain
operations that
might be conducted in processing a given signal for potentially improved
signal:noise ratio
(SNR), an MRS signal processor employing multiple steps providing significant
MRS signal
quality enhancement, in particular with respect to improved SNR for multi-
channel single
voxel pulse sequence acquisitions, have yet to be sufficiently automated to
provide robust
utility for efficient, mainstream clinical use, such as in primary
radiological imaging centers
without sophisticated MR spectroscopists required to process and interpret MRS
data. This
is believed to be generally the case as a shortcoming for many such in vivo
MRS exams in
general. Such shortcomings have also been observed in particular relation to
the unique
challenge of providing a robust MRS diagnostic system for diagnosing medical
conditions or
otherwise chemical parted magic 2019_01_03 Free Activators within relatively small voxels, areas of high
susceptibility
artifact potential, and in particular with respect to unique challenges of
performing MRS in


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voxels within intervertebral discs (including with further particularity,
although without
necessary limitation, of the lumbar spine). In solving many of these
challenges according to
certain aspects of the present disclosure, such as those providing particular
utility for
diagnosing discogenic low back pain and/or chemical environments within discs,
additional
beneficial advances have glary utilities crack download Free Activators been made that are also considered more broadly
applicable to
MRS in general, and as may become adapted for many specific applications, as
are also
herein disclosed.
[] Certain aspects of the current disclosure therefore relate to new
and
improved system approaches, techniques, processors, and methods for conducting
in vivo
clinical magnetic resonance spectroscopy (MRS) on human intervertebral discs,
in particular
according to a highly beneficial mode of this disclosure for using acquired
MRS information
to diagnose painful and/or non-painful discs associated with chronic, severe
axial lumbar (or
"low") back pain associated with degenerated disc disease (or "DDD pain"). For
purpose of
helpful clarity in this disclosure, parted magic 2019_01_03 Free Activators, the current aspects, modes, embodiments,
variations, and
features disclosed with particular benefits for this purposed are generally
assigned the label
"DDD-MRS." However, other descriptors may be used interchangeably as would be
apparent
to one of ordinary skill in context of the overall disclosure. It is also
further contemplated
within the scope of this present disclosure that, while this disclosure is archicad student to provide
particular benefit for use involving such human intervertebral discs (and
related medical
indications and purposes), the novel approaches herein described are also
considered more
broadly and applicable to other regions of interest and tissues within the
body of a subject,
and various medical indications and purposes. For purpose of illustration,
such other regions
and purposes may include, without limitation: brain, breast, heart, prostate, parted magic 2019_01_03 Free Activators,
GI tract, tumors,
degeneration and/or pain, parted magic 2019_01_03 Free Activators, inflammation, neurologic disorders, alzheimers, etc.
Various aspects of this disclosure relate to highly beneficial
advances in
each of three aspects, and their various combinations, useful in particular
for conducting a
DDD-MRS exam: (1) MRS pulse sequence for generating and acquiring robust MRS
spectra;
(2) signal processor configured to improve signal-to-noise ratio (SNR) of the
acquired MRS
spectra; and (3) diagnostic processor configured to use information from the
acquired and
processed MRS spectra for diagnosing painful and/or non-painful discs on which
the MRS
exam is conducted in a DDD pain patient.


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[] Several configurations and techniques related to the DDD-MRS
pulse
sequence and signal processor have been created, developed, and evaluated for
conducting
3T (or other suitable field strength) MRS on human intervertebral discs for
diagnosing DDD
pain. A novel "DDD" MRS pulse sequence was developed and evaluated for this
purpose,
and with certain parameters specifically configured to allow robust
application of the signal
processor for optimal processed final signals in a cooperative relationship
between the pulse
sequence and post-signal processing conducted. These approaches can be used,
for example,
with a 3 Tesla (3T) "Signa" MR system commercially available from General
Electric (GE).
Highly beneficial results have been observed using the current disclosed
application
technologies on this particular MR platfoini, as has been demonstrated for
illustration
according to Examples provided herein, and it is to be appreciated that
applying the present
aspects of this present disclosure in combination with this one system alone
is considered to
propose significant benefit to pain management in patients requiring
diagnosis. Accordingly, parted magic 2019_01_03 Free Activators,
various aspects of the present disclosure are described by way of specific
reference to
configurations and/or modes of operation adapted for compatible use with this
specific MR
system, and related interfacing components such as spine detector coils, in
order to provide a
thorough understanding of the disclosure. It is to be appreciated, however,
that this is done
for purpose of providing useful examples, and though significant benefits are
contemplated
per such specific example applications to that system, this is not intended to
be necessarily so
limited and with broader scope contemplated. The current disclosure
contemplates these
aspects broadly applicable according to one of ordinary skill to a variety of
MR platforms
commercially available that may be different suitable field strengths or that
may be
developed by various different manufacturers, and as may be suitably adapted
or modified to
become compatible for use with such different systems by one of ordinary skill
(with
sufficient access to operating controls of such system to achieve this).
Various novel and
beneficial aspects of this present disclosure are thus described herein, as
provided in certain
regards under the Examples also herein disclosed.
[] A DDD-MRS sequence exam is conducted according to one example
overview description as follows. A single three dimensional "voxel," typically
a rectangular
= volume, is prescribed by an operator at a control consul, using 3 imaging
planes (mid-
sagittal, coronal, axial) to define the "region of interest" (ROI) in the
patient's body, such as


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shown in FIGS. 1A-C, for MR excitation by the magnet and data acquisition by
the
acquisition channel/coils designated for the lumbar spine exam within the
spine detector coil
assembly. The DDD-MRS pulse sequence applies a pulsing magnetic and
radiofrequency to
the ROI, which causes single proton combinations in various chemicals within
the ROI to
resonate at different "signature resonant frequencies" across a range. The
amplitudes of
frequencies at various locations along this range are plotted along a curve as
the MRS
"spectrum" for the ROI. This is done iteratively across multiple acquisitions
for a given
ROI, typically representing over 50 acquisitions, often or more
acquisitions, and often
between about and about acquisitions, such as between and
acquisitions for
a given exam of a ROI. One acquisition spectrum among these iterations is
called a "frame"
for purpose of this disclosure, though other terms may be used as would be
apparent to one of
ordinary skill. These multiple acquisitions are conducted in order to average
their respective
acquired spectra/frames to reduce the amplitudes of acquired signal components
representing
noise (typically more random or "incoherent" and thus reduced by averaging)
while better
maintaining the amplitudes of signal components representing target resonant
chemical
frequencies of diagnostic interest in the ROI (typically repeatable and more
"coherent" and
thus not reduced by averaging). By parted magic 2019_01_03 Free Activators noise while maintaining true
target signal, or at
least resulting in less relative signal reduction, this multiple serial frame
averaging process is
thus conducted for the primary objective to increase SNR. These acquisitions
are also
conducted at various acquisition channels selected at the detector coils, such
as for example 6
channels corresponding with the lumbar spine area of the coil assembly used in
the Examples
(where for example 2 coils may be combined for each channel).
[] The 3T MRI Signa system ("Signa" or "3T Signa"), in standard
operation
conducting one beneficial mode of DDD-MRS sequence evaluated (e.g. Examples
provided
herein), is believed to be configured to average all acquired frames across
all acquisition
channels to produce a single averaged MRS curve for the ROI. This unmodified
approach
has been observed, including according to the various Figures and Examples
provided herein,
to provide a relatively low signal/noise ratio, with low confidence in many
results regarding
data extraction at spectral regions of diagnostic interest, such as for
example and in particular
regions associated with proteoglycan or "PG" (n-acetyl) and lactate or lactic
acid (LA).
Sources of potential error and noise inherent in this imbedded signal
acquisition and


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processing configuration of the typical MR system, for example were observed
in conducting
the DDD-MRS pulse sequence such as according to the Examples. These various
sources of
potential error or signal-to-noise ratio (SNR) compromise were determined to
be mostly
correctable - either by altering certain structures or protocols of coil,
sequence, or data
acquisition, or in post-processing of otherwise standard protocols and
structures used.
Among these approaches, various post-acquisition signal processing approaches
were
developed and observed to produce significantly improved and highly favorable
results using
otherwise un-modified operation pre-processing. In particular, various
improvements
developed and applied under the current post-signal processor disclosed herein
have been
observed to significantly improve signal quality and SNR.
[ Certain
such improvements advanced under the post-signal processor
configurations disclosed herein include embodiments related to the following:
(1)
acquisition channel selection; (2) phase error correction; (3) frequency error
correction; (4)
frame editing; and (5) apodization. These modules or steps are typically
followed by channel
averaging to produce one resulting "processed" MRS spectrum, when multiple
channels are
retained throughout the processing (though often only one channel may be
retained). These
may also be conducted in various different respective orders, though as is
elsewhere further
developed frame editing will typically precede frequency error correction. For
illustration,
one particular order of these operations employed for producing the results
illustrated in the
Examples disclosed herein are provided as follows: (1) acquisition channel
selection; (2)
phase correction; (3) apodization; (4) frame editing; (5) frequency
correction; and (6)
averaging.
[] While
any one of these signal processing operations is considered highly
beneficial, their combination has been observed to provide significantly
advantageous results,
and various sub-combinations between them may also be made for beneficial use
and are also
contemplated. Various illustrative examples are elsewhere provided herein to
illustrate
sources of error or "noise" observed, and corrections employed to improve
signal quality.
Strong signals typically associated with normal healthy discs were evaluated
first to assess
the signal processing approach, parted magic 2019_01_03 Free Activators. Signals from the Signa that were considered
more
"challenged" for robust data processing and diagnostic use were evaluated for
further


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development to evaluate if more robust metabolite signal can be elicited from
otherwise
originally poor SNR signals from the Signa.
[] Additional description further developing these aspects according
to
additional embodiments, and other aspects, is provided below.
[] Spine Detector Coil and Patient Positioning
[] A typical DDD-MRS exam according to the present embodiments will
be
conducted in an MR scanner in which the patient lies still in a supine
position with a spine
detector coil underneath the patient's back and including the lower spine.
While this scanner
applies the magnetic and RF fields to the subject, the spine detector coil
functions as an
antenna to acquire signals from resonating molecules in the body. The primary
source of
MRS signals obtained from a Signa 3T MR scanner, according to the physical
embodiments
developed and evaluated in the Examples herein this disclosure, are from the
GE HD CTL
Spine Coil. This is a "receive-only" coil with sixteen coils configured
into eight
channels. Each channel contains a loop and saddle coil, and the channels are
paired into
sections. For lumbar (and thoracic) spine coverage, such as associated with
lumbar DDD
pain diagnosis, sections 4, 5, and 6 are typically deployed to provide six
individual channel
signals, as shown for example in FIG. 2.
[] Defining the Voxel (Voxel Prescription)
[] Certain embodiments of this disclosure relates principally to
"single
voxel" MRS, where a single three dimensional region of interest (ROT) is
defined as a
"voxel" (VOlumetric piXEL) for MRS excitation and data acquisition, parted magic 2019_01_03 Free Activators. The
spectroscopic
voxel is selected based on T2-weighted high-resolution spine images acquired
in the sagittal,
coronal, and axial planes, as shown for example in FIGS. 1A-C. The patient is
placed into
the scanner in a supine position, head first. The axial spine images acquired
are often in a
plane oriented with disc angle (e.g. may be oblique) in order to better
encompass the disc of
interest. This voxel is prescribed within a disc nucleus for purpose of using
acquired MRS
spectral data to diagnose DDD pain, according to the present preferred
embodiments. In
general for DDD-MRS applications evaluating disc nucleus chemical
constituents, the
objective for voxel prescription is to capture as much of the nuclear volume
as possible (e.g.
maximizing magnitude of relevant chemical signals acquired), while restricting
the voxel
borders from capturing therewithin structures of the outer annulus or
bordering vertebral


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body end-plates (the latter being a more significant consideration, where
lipid contribution
may be captured and may shroud chemical spectral regions of interest such as
lactate or
alanine, as further developed elsewhere herein). In fact, the actual operation
may not exactly
coincide with acquiring signal from only within the voxel, and may include
some bordering
region contribution. Thus some degree of spacing between the borders and these
structures is
often desired. These typical objectives may be more difficult to achieve for
some disc
anatomies than others, e.g, parted magic 2019_01_03 Free Activators. relatively obliquely angled discs. For example, L5-
S1 may be
particularly challenging because in some patients it can frequently be highly
angulated,
irregularly shaped, and collapsed as to disc height.
[ In certain voxel prescriptions, the thickness is limited by the
scanner's
ability to generate the magnetic gradient that defines the Z-axis (axial
plane) dimension. For
example, a minimum thickness limit is pre-set to 4mm on the GE Signa 3T. While
such pre-
set limits of interfacing, cooperative equipment and related software may
result in limits on
the current application's ability to function in that environment outside of
these limits, the
broad aspects of the current disclosure should not be considered necessarily
so limited in all
cases, and functionality may flourish within other operating ranges perhaps
than those
specifically indicated as examples herein, such as in cases where such other
imparted
limitations may be released.
[ These usual objectives and potential limitations in mind, typical
voxel
dimensions and volumes (Z-axis, X-axis, Y-axis, Vol) may be for example 5mm
(thick) by
14mm (width) by 16mm (length), and cc, though one may swiftshader 2.0 download any or all of
these
dimensions by operator prescription to suit a particular anatomy or intended
application. The
Z-axis dimension is typically limited maximally by disc height (in order to
exclude the end-
plates, described further herein), and minimally by either the set minimum
limitations of the
particular MR scanner and/or per SAR safety considerations, in many disc
applications (such
as specific indication for pain diagnosis or other assessment of disc
chemistry described
herein). This Z-axis dimension will typically be about 3mm to about 6 mm
(thick), more
typically between about 4mm to about 6mm, and most typically will be suitable
(and superantispyware professional be
required to be, per anatomy) between about 4mm to about 5mm. The other
dimensions are
typically larger across the disc's plane, and may be for example between about
15mm to
about 20mm (width and/or length), as have been observed suitable ranges for
most observed


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cases (e.g. per the Examples herein). While the higher dimension of these
ranges is typically
limited only by bordering tissues desirable to exclude, the opportunity for
patient motion to
alter the relative location of the target voxel relative to actual anatomy may
dictate some
degree of "spacing" from such bordering structures to ensure exclusion. The
smaller
dimensions of the ranges are more related to degraded signal quality that
comes with
excessively small voxel volume, whereas signal amplitude will typically be
directly related to
voxel dimension and volume. Accordingly, voxels within discs will generally
provide robust
results, at least with respect to signal quality, at volumes of at least about
.5cc, and in many
cases at least about cc or 1 cc. This typically will be limited by
bordering anatomy to up to
about 2ccs, or in some less typical cases up to about 3ccs for exceptionally
large discs.
These voxel volume ranges will typically be achieved with various combinations
of the
typical axis dimensions as also stated above.
[] Also according to the typical voxel prescription objectives and
limitations
stated above, an initial prescription may not be appropriate for achieving
acceptable results,
though this may not be known until a sequence is begun to allow observation of
acquired
signal quality. Accordingly, further aspects of the present disclosure
contemplate a voxel
prescription protocol which prescribes a first prescription, monitors results
(either during
scan or after completion, or via a "pre-scan" routine for this purpose), and
if a lipid signature
or other suspected signal degradation from expected results is observed, re-
prescribe the
voxel to avoid suspected source of contaminant (e.g. make the voxel smaller or
adjust its
dimensions, parted magic 2019_01_03 Free Activators, tilt, or location) and re-run an additional DDD-MRS acquisition
series (retaining
the signal considered more robust and with least suspected signal degradation
suspected to be
voxel error). According to still a further mode, a pre-set protocol for re-
prescribing in such
circumstances may define when to accept the result vs. continue re-trying. In
one
embodiment, the voxel may be re-prescribed and acquisition series re-run once,
or perhaps
twice, and then the best result is to be accepted. It is to be appreciated, as
with many
technology platforms, that operator training and techniques in performing such
user-
dependent operations may be relevant to results, and optimal (or conversely
sub-optimal)
results may track skill levels and techniques used.
[] To further illustrate this parted magic 2019_01_03 Free Activators aspect of the present
disclosure, the
example of a single voxel prescription according to the typical three planar
slice images is


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shown in Figures 1A-1C as follows. More specifically, Fig. 1 A shows a coronal
view
oriented aspect parted magic 2019_01_03 Free Activators the voxel prescription. Fig. 1B shows a sagittal view
oriented aspect of the
voxel prescription. Fig. 1C shows an axial view oriented aspect of the voxel
prescription.
[] The "DDD" MRS Pulse Sequence - PRESS
[ The DDD-MRS pulse sequence according to one embodiment shares
certain similarities, though with certain differences and modifications
defined herein, with
another MRS pulse sequence called "PROSE". PROSE is primarily intended for use
for
diagnosing prostate cancer, and is approved for use and sale and available
from GE on T
GE MR systems. The DDD-MRS pulse sequence of the present embodiments, and
PROSE
for further reference, employ a sequence approach called Point RESolved
Spectroscopy
(PRESS). This involves a double spin echo sequence that uses a excitation
pulse with
two slice selective refocusing radio frequency (RF) pulses, combined with
3D chemical
shift imaging (CSI) phase encoding gradients to generate 3-D arrays of
spectral data or
chemical shift images. Due to the small size, irregular shape, and the high
magnetic
susceptibility present when doing disc spectroscopy for DDD pain, the 3D phase
encoding
option available under PROSE is not an approach typically to be utilized under
the current
disclosed version of DDD-MRS sequence, and single voxel spectra are acquired
by this
version embodiment of DDD-MRS pulse sequence. This unique relative
configuration for
the DDD-MRS pulse sequence can be accomplished by setting the user control
variables
(CVs) for the matrix acquisition size of each axis to 1 (e.g., in the event
the option for other
setting is parted magic 2019_01_03 Free Activators available). Further aspects of pulse sequence approaches
contemplated are
disclosed elsewhere herein. It is to be appreciated that while the modified
PRESS approach
herein described is particularly beneficial, other approaches may be taken for
the pulse
sequence according to one of ordinary skill consistent with other aspects and
objectives
herein login bitdefender and without departing from the broad aspects of intended
scope herein.
[] Water and Lipid Signal Suppression - CHESS
[] In another sequence called "PROBE" also commercially available
from
GE, and which is a CSI sequence used for brain spectroscopy, the lipid/fat
signals are
believed to be resolved through the use of long TE (ms) periods and 2
dimensional
transformations (2DJ). These acquisition and signal processing techniques are
believed to be
facilitated by the large voxel volumes prescribed in the brain as well as the
homogeneity of


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the brain parted magic 2019_01_03 Free Activators resulting in relatively narrow spectral line widths. In the
prostate region
targeted by the different pulse sequence of PROSE, however, the voxel
prescriptions are
much smaller and it is often impossible to place the voxel so as to assuredly
exclude tissues
that contain lipid/fat. Therefore, two water and lipid suppression approaches
are available
and may be used, if warranted, in the PROSE sequence: "BASING" and "SSRF"
(Spectral
Spatial Radio Frequency). An even more challenging environment of bordering
lipid and
reduced homogeneity has been observed with the current DDD pain application of
the
lumbar intervertebral discs where the current ROT within disc nuclei are
closely bordered by
vertebral bodies with bone marrow rich in lipid content. However, due both to
the desire to
use short TE times (e.g. 28ms) for the current DDD pain application in lumbar
spine, and the
desire to observe MRS signatures of other chemicals within parted magic 2019_01_03 Free Activators nuclei that
may overlap with
lipid signal contribution along the relevant DDD-MRS spectrum, these
water/lipid
suppression approaches as developed for brain and prostate application are not
necessarily
optimized for DDD-MRS application in many circumstances. While a SSRF
suppression
approach for lipid resonances may be employed in the DDD-MRS sequence, the
narrow band
RF pulse required for this may require a long RF period and amplitude that
will exceed the
SAR level for many MR systems.
[] Water suppression is also provided by a CHESS sequence
interleaved or
otherwise combined in some manner with the PRESS sequence in order to provide
appropriate results. Optimization of the residual water spectral line for
frequency correction
is done, according to one highly beneficial further aspect of the present
disclosure, with the
setting prescribed for the third flip angle. The angle is lowered to reduce
the water
suppression function which templatetoaster 7 crack free download Free Activators the residual water spectral line
amplitude. Conversely
higher relative third flip angles will increase water suppression for reduced
water signal in an
acquired MRS spectrum. A particular flip angle for this purpose may be for
example about
, though may be according to other examples between about 45 degrees and
about
degrees (or as much as about degrees). Accordingly, parted magic 2019_01_03 Free Activators, in one aspect, the
flip angle may be
for example at least about 45 degrees. In another aspect, the flip angle may
be up to
degrees or even up to degrees. Notwithstanding these examples, an expanded

experimental data set of 79 discs in 42 subjects represented under Example 3
included robust,
reliable results across this population with an average flip angle of about
or degrees


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+/- about 33 degrees. Moreover, a later component of that population conducted
with further
refinements revealed a majority of cases suitable at a flip angle between
about 65 degrees
and about degrees, and in fact with most found to be sufficient at about
85 degrees. It is
to be appreciated that despite these specific number and range examples, and
robust results
observed therefrom, it is also believed that flip angles within about 5 or 10
degrees apart are
likely to produce susbstantially similar results for purposes intended herein.
[] This flip angle aspect of the present disclosure is another
example where
some degree of customization may be required, in order to optimize water
signal for a given
disc, in a given particular MR system. As some discs may be more dehydrated or
conversely
more hydrated than others, parted magic 2019_01_03 Free Activators, the water suppression may be more appropriate at
one level for
one disc, and at another level for another disc. This may require some
iterative setting and
acquisition protocol to optimize, whereas the angle example described herein
is considered
appropriate for most circumstances and may be a pre-defined starting place for
"first try."
[] For further clarity and understanding of the present DDD-MRS
pulse
sequence embodiments introduced above and also elsewhere herein described,
FIG. 3A
shows an example of a CHESS water suppression pulse sequence diagram, whereas
FIG. 3B
shows an example of a combined CHESS - PRESS pulse sequence diagram.
[] Very Selective Saturation (VSS) Bands
[] The volume excitation achieved using PRESS takes advantage of
three
orthogonal slices in the form of a double spin echo to select a specific
region of interest. In
some embodiments, the range of chemical shift frequencies (over Hz for
proton at T)
is not insignificant relative to the limited band width of most excitation
pulses (
Hz). The result can be a misregistration of the volume of interest for
chemical shift
frequencies not at the transmitter frequency. Thus, when parted magic 2019_01_03 Free Activators PRESS volume is
resolved by parted magic 2019_01_03 Free Activators, the chemical levels may be not only dependent on tissue level, T1 and T2,
but also
dependent on location within the volume of interest. In some embodiments, due
to
imperfections in the RF pulse, out of volume excitation may occur which can
present signals
from chemicals that are not in the frequency/location range of interest.
[] Accordingly, another feature that is contemplated according to a
further
mode of the DDD-MRS sequence is the use of very selective saturation (VSS)
pulses. This
is often beneficial to deploy for example for removal of signal contamination
that may arise


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from chemical shift error due to the presence of lipids within the voxel as
well as outside the
selected ROT or voxel in the disc nuclei. In the default operating mode of one
DDD-MRS
sequence approach, in some regards sharing some similarities with PROSE, parted magic 2019_01_03 Free Activators, for
example,
multiple pairs of VSS RF suppression bands are placed symmetrically around the
prescribed
DDD-MRS voxel. In certain embodiments, the voxel in this approach is
oversized, such as
for example by % (e.g. PRESS correction factor = about ). The DDD-MRS
sequence
according to this mode uses the VSS bands to define the actual DDD-MRS volume.
It is
believed that up to about six additional VSS bands may be prescribed (each
consisting of
three VSS RF pulses) graphically in PROSE, with the goal of reducing the
chemical shift
error that can occur within the voxel as well as suppress excitation of out of
voxel tissue
during the PRESS localization of the voxel. According to some observations in
applying
DDD-MRS to disc spectroscopy, these additional graphic VSS pulses were found
to not
significantly improve the volume selection. In other observations, parted magic 2019_01_03 Free Activators, some
benefit is suspected
to have resulted. Accordingly, while they may provide benefit in certain
circumstances, they
also may not be necessary or even desired to be used in others.
[] FIG. 3C shows a schematic diagram of certain aspects of a
combined
CHESS ¨ Parted magic 2019_01_03 Free Activators PRESS pulse sequence diagram also consistent with certain aspects
of the
present disclosure, parted magic 2019_01_03 Free Activators. As also shown for further illustration in multiple
respective image planes
in FIGS. 4A-B, multiple VSS bands are placed around the voxel prescription in
each plane to
reduce out of voxel excitation and chemical shift error present during the
PRESS localization
of the voxel.
[] PRESS timing parameters
[] For purpose of comparative reference, the echo time (TE) of about
ms
is believed to be the default selection typically used for PROSE data
acquisitions. This echo
time is typically considered too long for DDD-MRS pulse sequence applications
for parted magic 2019_01_03 Free Activators robust disc spectra due to the small voxel volume and shorter T2
relaxation times of
the chemical constituents of lumbar intervertebral discs, leading to a
dramatic decrease in
signal to noise in long echo PRESS spectra. Therefore a shorter echo time
setting for the
scanner, such as for example about 28 milliseconds, is generally considered
more appropriate
and beneficial for use in the current DDD-MRS sequence and DDD pain
application (though
this may be varied as would be apparent to one of ordinary skill based upon
review of this


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total disclosure and to suit a particular situation). A frame repetition time
(TR) of for
example about ms provides sufficient relaxation of the magnetic dipoles
in the ROT and
leads to reasonable acquisition times and is believed to represent a
beneficial compromise
between short acquisition times and signal saturation at shorter values of TR
(though this
may also be varied, as also elsewhere herein described), parted magic 2019_01_03 Free Activators. Other appropriately
applicable
operating parameter settings for PRESS spectra suitably applicable to the DDD-
MRS
sequence may be, for example: number of data points equal to about
number of
repetitions equal to aboutand example typical voxel size of about 4mm x
18mm x
16mm (cc). Furthermore, first, second, and third flip angles of PRESS for
the current
DDD-MRS sequence embodiment may be for example 90,andrespectively
(though these may slightly vary, and user-defined settings may not always
reflect actual
angle ¨ for example the latter two values may be exchanged with or represent
one example of
an actual result of a degree setting).
[] Summary of User Control Variable (CV) Examples for DDD-MRS
Sequence parted magic 2019_01_03 Free Activators The foregoing disclosure describes various user controllable
sequence
settings observed to be appropriate and of particular parted magic 2019_01_03 Free Activators for use in an
example DDD-
MRS sequence according to the current disclosure and for use for diagnosing
DDD pain, as
contemplated under the preferred embodiments herein. These are further
summarized in
Table 1 appended herewith at the end of this disclosure.
[] One or more of these CVs may comprise modifications from similar
settings that may be provided for another CHESS-PRESS or CHESS-VSS-PRESS pulse

sequence, such as for example PROSE, either as defaults or as user defined
settings for a
particular other application than as featured in the various aspects herein
this disclosure.
These CV settings, in context of use as modifications generally to a sequence
otherwise
sharing significant similarities to PROSE, are believed to result in a highly
beneficial
resulting DDD-MRS sequence for the intended purpose of later signal
processing, parted magic 2019_01_03 Free Activators, according
to the DDD-MRS signal processor embodiments herein described, and performing a

diagnosis of DDD parted magic 2019_01_03 Free Activators in discs examined (the latter according for example to
the DDD-MRS
diagnostic processor aspects and embodiments also herein disclosed). However,
it is also
appreciated that these specific settings may be modified by one of ordinary
skill and still


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provide highly beneficial results, and are also contemplated within the broad
intended scope
of the various aspects of this present disclosure.
[] Data Acquisition of DDD-MRS Pulse Sequence
[] The signal detected in the MR spectrometer in the receiving
"detector"
coil assembly, after exposing a sample to a radio frequency pulse, is called
the Free
Induction Decay (FID) for purpose of this disclosure, parted magic 2019_01_03 Free Activators. In modem MR
spectrometers the MR
signal is typically detected using quadrature detection. As a result, the
acquired MR signal is
composed of two parts, often referred as real and imaginary parts of the FID.
A schematic
example of the time domain FID waveform is shown in FIG. 5, which shows the
real (Sx)
and imaginary (Sy) parts of an FID (right) that correspond to x and y
components of the
rotating magnetic moment M (left).
[] FIDs are generated at the period defined by TR. Thus a TR of
about
milliseconds, according to the example embodiment described above, equals a
rate of about 1
Hz (about one FID per second). The FID signal received from each coil channel
is digitized
by the scanner to generate a point complex number data set or acquisition
frame. An
MRS scan session consists of a number of frames of unsuppressed water FIDs
(such as for
example may be about 16 frames) and up to or more (as may be defined by an
operator
or setting in the pulse sequence) frames of suppressed water FIDs, which
together are
considered an acquisition series. The unsuppressed water FIDs provide a strong
water signal
that is used by the signal processing to determine which coils to use in the
signal processing
scheme as well as the phase information from each coil (and in certain
embodiments may
also be used for frequency error correction). However, due to gain and dynamic
range in the
system these high water content unsuppressed frames do not typically provide
appropriate
resolution in the target biomarker regions of the associated spectra to use
them for diagnostic
data purposes. The suppressed water FIDs are processed by the DDD-MRS
processor to
obtain this spectral information, although the unsuppressed frames may be used
for certain
processing approaches taken by the processor.
[] For further illustration, FIG. 6 shows a plot of all the FIDs
obtained in a
DDD-MRS pulse sequence scan according to certain present illustrative
embodiments, and is
an amplitude plot of complex data from a standard DDD-MRS acquisition with the
y-axis


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representing the magnitude of FID data and the x-axis representing serial
frame count over
time.
[] DDD-MRS Pulse Sequence Data Transfer from MR System to Post-
Processor
[] The MR scanner generates the FIDs using the defined sequences to
energize the volume of interest (VOI), digitizes them according to the defined
data
acquisition parameters, and stores the data, typically as floating point
numbers. While this
data may be packaged, e.g. in "archive file," and communicated in various
formats and
methods, one example is provided here. A data descriptor header file (DDF)
with all the
aforementioned parameters along with voxel easeus mobisaver license code data is appended to
the data to
generate the archive file, parted magic 2019_01_03 Free Activators. Examples of certain parameters provided in a DDF,
are as
follows:studyID (String); seriesNum (Integer parted magic 2019_01_03 Free Activators assigned Series Number);
studyDate(String
date code); seriesDesc (String for series description); rootName (String);
nSamps (Integer for
number of complex samples, typically ); nFrames (Integer for number of
frames or
reps); coilName (String); pulseSeqName (String); Te (Float, echo time, in ms);
Tr (Float,
repetition time, in ms); TxFreq (Float, in MHz); nSatBands (Integer, number of
saturation
bands); voxTilt (Float, voxel tilt about x-axis, in degrees); voxVol (Float,
Voxel volume in
cc); voxX (Float, Voxel X dimension, in mm); voxY (Float, Voxel Y dimension,
in mm);
voxZ (Float, Voxel Z dimension, in mm), parted magic 2019_01_03 Free Activators. The archive file can include data
received from
the MR scanner that is representative of the anatomy of a patient (e.g.,
representative of the
chemical makeup of tissue inside the area of interest inside an intervertebral
disc of the
patient's spine).
[] The archive file may then be transferred to another computer
running an
application written in a language, such as for example Matlab Ra (e.g.
with "Image
Processing Toolbox" option, such as to generate time-intensity plots such as
shown in
various Figures herein), parted magic 2019_01_03 Free Activators, which opens the archive file. The Matlab application
may be user-
configurable, or may be configured as full or partial executables, and is
configured to signal
process the acquired and transferred DDD-MRS information contained in the
archive file,
such as according to the various signal processing embodiments herein. Other
software
packages, such as "C," "C+," or "C++" may be suitably employed for similar
purposes. This
application, subsequently referred to as the DDD-MRS signal processor, parses
information


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pertinent to the signal processing of the data from the data description
header, and imports
the FID data acquired at each detector coil for subsequent signal processing.
It will be
understood that the DDD-MRS signal processor can be implemented in a variety
of manners,
such as using computer hardware, firmware, or software, or some combination
thereof In
some embodiments, the DDD-MRS signal processor can comprise a computer
processor
configured to execute a software application as computer-executable code
stored in a non-
transitory computer-readable medium. In some embodiments, the computer
processor can be
part of a general purpose computer. In some embodiments, the DDD-MRS signal
processor
can be implemented using specialized computer hardware such as integrated
circuits instead
of computer software. It will be understood that the DDD-MRS signal processor,
as well as
other components described herein that may be implemented by a computer, can
be
implemented by multiple computers connected, for example, by a network or the
internet.
Thus, algorithms, processes, sequences, calculations, and tasks, etc.
described herein can be
divided into portions to be performed by multiple computer processors or other
hardware
located on multiple physically distinct computers. Also, some tasks that are
described herein
as being performed by distinct computers or systems may be performed by a
single computer
or a parted magic 2019_01_03 Free Activators integrated system.
[] The archive file and related MRS data may be communicated via a
number of available networks or methods to external source for receipt,
processing, or other
form of hotspot shield elite crack 2019. In one particular typical format and method, the information is
communicated
via picture archiving and communication system (PACS) that has become
ubiquitous for
storing and communicating radiologic imaging information. In addition to the
archive file
with DDF and stored MRS data, accompanying MRI images may also be stored and
communicated therewith, e.g. in standardized "digital imaging and
communications in
medicine" or "DICOM" format.
[] The data transfer described may be to a local computer processor
for
processing, or more remotely such as via the web (typically in secure format).
In alternative
to data transfer of acquired MRS data pre-processing to an external system for
post-
processing as described above, e.g. MRS signal processor and diagnostic
processor aspects of
this disclosure, all or a portion of the various aspects of the present
embodiments may be
installed or otherwise integrated within the MR system itself, e.g, parted magic 2019_01_03 Free Activators. a computer
based


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controller or processor embedded therewithin or otherwise connected thereto,
for operation
prior to packaging results for output (and any remaining portions might be
performed
peripherally or more remotely).
[] DDD-MRS Signal Processing
[] Upon the acquisition of all MRS data from a DDD-MRS pulse
sequence
exam, according to certain aspects of the present embodiments, parted magic 2019_01_03 Free Activators, the MR scanner
system will
typically provide the operator with a spectral image that is the averaged
combination of all
frames across all the 6 detection channels (coils). An example of such a
waveform from an
MRS pulse sequence exam acquired for an ROI in a disc nucleus via a GE Signa
3T MR
system is shown in FIG. 7, which shows a typical scanner-processed spectral
signal plot of
combined, averaged channels. FIG. 8 shows the magnitude only (no correction)
MRS
spectral images of each of the six channels which are aggregated to form the
output from the
example MR system as shown in FIG. 7, and thus this raw uncorrected individual
channel
spectral data output provides the input to the DDD-MRS signal processor of the
present
embodiments.
[] According to one highly beneficial mode, the DDD-MRS signal processor
is
configured to conduct a series of operations in temporal fashion as described
herein, and as
shown according to the present detailed embodiments in the flow charts
illustrated in general
to increasing detail for the various component modes and operable steps in
FIGS. 9A-C.
More specifically, FIG. 9A shows a general schematic overview for the flow of
a diagnostic
system 2 that includes a signal processor 4 and a diagnostic processor 6.
Signal processor
includes various sub-components and processors that carry out certain steps,
such as a
channel selector that conducts channel or "coil" selection step 10, phase
corrector that does
phase correction step 20, apodizer that conducts apodizer step 30, domain
transformer that
conducts the domain transform step 44 such as from time domain to frequency
domain,
frame editor that conducts frame editing steps 50, frequency corrector that
conducts
frequency correction steps 60, and channel combiner that conducts combining or
averaging
steps 70 to aggregate retained channels into one final post-processed spectral
results (not
shown). Following signal processing steps 6, a diagnostic processor conducts
diagnostic
processing of the signal processed signals through data extraction steps
diagnostic
algorithm application stepsand patient or diagnostic report generation
While this


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configuration is considered highly beneficial, these same or similar tasks may
be performed
in different order, as would be apparent to one of ordinary skill.
[] For illustration, FIGS 9B-C show further details regarding some of
these
specific steps, and also illustrate a different order than has been shown and
referenced to
FIG. 9A. More specifically, the signal processor 4 in shown to include the
main primary
steps shown in FIG. 9A, but in finer detail and different order. It some
embodiments, the
steps shown in FIGS. 9A-C may be performed in an order different than those
shown in
FIGS. 9A-C. Also, in some embodiments, steps that are shown in FIGS. 9A-C can
be
omitted, combined with other steps, or divided into additional sub-steps.
Additionally, in
some cases, additional steps not specifically shown in FIGS. 9A-C can be
performed in
addition to the steps shown in FIGS. 9A-C.
[] Channel selection includes the following steps: signal power
measurement
step 11 measures signal power for SNR calculation, as shown here in the
specific
embodiment in first points of FID with unsuppressed water. Noise power
measurement
step 13 measures noise in the last points, for example, of the FID with
unsuppressed
frames, parted magic 2019_01_03 Free Activators. SNR estimate 15 is then conducted, at which point thereafter channel
selection step
17 is conducted per the channel with the maximum or highest signal, parted magic 2019_01_03 Free Activators. Channel
selection
includes an additional step 18 where additional channels are selected if
within range of the
strongest, e.g. about 3dB. Upon completing channel selection, an index of
selected channels
is generated (step 19).
[] Frame editing operation 50 is also shown, with transformation of
unsuppressed water frame to frequency domain 51, parted magic 2019_01_03 Free Activators, locate water peak in +1- 40Hz
per peak
location step 53, frame parted magic 2019_01_03 Free Activators level calculation 55, frame frequency error
and store 57, parted magic 2019_01_03 Free Activators,
and actual frame selection step 59 based upon minimum confidence level
threshold (e.g. .8)
Phase correction 20 is also done per applying 21 1st order linear curve fit to
the FID of each
unsuppressed water frame (e.g. n=16)., obtaining average of zero order terms
from the curve
fit parted magic 2019_01_03 Free Activators, and rotate 25 all suppressed water frame FIDs by zero order parted magic 2019_01_03 Free Activators.
Apodization 30
includes for each selected channel and each frame indexed for frequency
correction 31, then
apply point boxcar function to the FID (step 33). In addition, frequency
correction 60
entails for each selected channel and each frame indexed for frequency
correction and
apodization 61, transform 63 the frame (FID) to the frequency of domain, parted magic 2019_01_03 Free Activators, and
locate the


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frequency error 65 for the frame as identified during frame editing. The
spectrum is shifted
67 by the frequency error value to frequency correct the spectrum. Step 69
adds frequency
corrected spectrum to many to spectral average for all selected channels.
[] As also shown in FIG. 9C for the diagnostic processor 6, data
extraction
involves opening the acquisition spectral average lot generated in
frequency correction,
scan step of spectral plot for metabolite features and apply to bins, and
record data bins
to acquisition metabolite The diagnostic algorithm itself involves
opening the
acquisition metabolite file extracted from spectral averageextract
metabolite
features required by diagnostic linear regression equations, and generate
acquisition
diagnostic score and store to file. Report generation includes open
acquisition diagnostic
score fillopen DICOM file for Patient ID associated with acquisition
diagnostic
score, extract sagittal image from the DICOM report, parted magic 2019_01_03 Free Activators, apply acquisition
score to
sagittal image for each scanned disc, and return sagittal image to DICOM
report
[] According to the current example embodiment, a first operation of the
DDD-
MRS processor assesses the SNR of each channel. This is done to determine
which channels
have parted magic 2019_01_03 Free Activators sufficiently robust signal to use for data processing and
averaging, parted magic 2019_01_03 Free Activators. The result
may produce one single channel that is further processed, or multiple channels
that are later
used in combination under multi-channel averaging. In the majority of acquired
signals
observed according to the Examples disclosed herein, only a subset of the 6
lumbar
acquisition channels were determined to be sufficiently robust for use.
However, the
standard system output averages all 6 channels. Accordingly, this filtering
process alone ¨
removing poor signal channels and working with only stronger signal channels ¨
has been
observed to dramatically improve processed spectra for diagnostic use in some
cases. While
various techniques may be suitable according to one of ordinary skill, parted magic 2019_01_03 Free Activators, and
thus contemplated
herein, according to the present illustrative embodiment the SNR is calculated
by obtaining
the average power in the first data points (the signal) and the last
points (the noise)
of the unsuppressed water FID. The unsuppressed water FIDs signals are used
because of the
strong water signal. The channel with the greatest SNR, and channels within a
predetermined
threshold variance of that strongest one, e.g. within about 3 dB for example,
are preserved for
further processing and as candidates for multi-channel averaging ¨ other
channels falling


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below this range are removed from further processing (though may be used for
further anyvid for android, yet removed from final results).
Further examples and embodiments for evaluating relative channel
quality are
provided as follows. One additional indication of channel quality that may be
observed and
used is the line width of the unsuppressed water signal based on the averaged
frequency and
phase corrected FFTs of the coil channels with the highest SNRs. This is
computed to serve
as a general indicator of signal quality as determined by the quality of the
shimming process
and to provide an estimate of the resolution we should expect in the chemical
shift spectrum.
Another indication of channel quality is the degree of water suppression. This
has utility in
determining the optimum degree of water suppression to apply in the
acquisition protocol.
The water suppression should leave enough residual water signal to use as a
reference to
reliably perform frame-by-frame frequency correction but not so much that
water signal
artifacts affect the chemical shift spectrum in the metabolite areas of
interest. Such artifacts
include simple spectral leakage as well as phase modulation sidebands due to
gradient
vibration induced Bo modulation.
[] Further to the present embodiments and per further reference to
FIGS. 9A-
B, a second operation conducted by the DDD-MRS processor is phase alignment,
or phase
error correction. This is performed to support coherent summation of the
signals from the
selected channels and the extraction of the absorption spectra. This is often
necessary, parted magic 2019_01_03 Free Activators, or at
least helpful, because in many cases a systemic phase bias is present in the
different
channels. This systemic phase bias is best estimated by analysis of the data
frames (e.g.
about 16 frames in the DDD-MRS pulse sequence of the current illustrative
detailed
embodiments) collected at the beginning of each scan without water
suppression. This
operation, according to one mode for example, analyzes the phase sequence of
the complex
samples and fits a polynomial to that sequence. A first-order (linear) fit is
used in one further
illustrative embodiment. This is believed to provide a better estimate of the
offset than
simply using the phase of the first sample, as is often done. This is because
eddy current
artifacts, if present, parted magic 2019_01_03 Free Activators, will be most prominent in the first part of the frame.
The offset of the
linear fit is the initial phase. Observation has indicated that the first
samples (75 mS at
the typical samples-per-second rate) typically provide reliable phase
data. The fit is
performed on each of the water-unsuppressed frames for parted magic 2019_01_03 Free Activators channel and the
mean phase of


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these is used to phase adjust the data for the corresponding channel. This is
accomplished by
performing a phase rotation of every complex sample in each frame to
compensate for the
phase offset as estimated above, setting the initial phase to zero.
[] The offset of the parted magic 2019_01_03 Free Activators fit is the phase bias with respect to
zero and the
slope is the frequency error with respect to perfect center-tuning on the
water signal. Only the
offset portion of the curve fit is used to phase correct the data. An
illustrative example of this
is shown in schematic form in FIG. 10, which shows phase angle before and
after phase
correction. The phase angle signal is shown as the dotted line. The solid line
is the least
squares fit estimate. The dashed line parted magic 2019_01_03 Free Activators the phase and frequency corrected
signal, parted magic 2019_01_03 Free Activators, though the
offset component is used to phase correct and frequency correction is
performed
subsequently in the temporal process according to the present DDD-MRS
processor
embodiment.
[] The real-part squared MRS spectral results of phase correction
for each of
all the six channels shown prior to correction in FIG. 8 is shown in FIG. 11,
with channels 1-
3 indicated from left to right at the top, and channels indicated from
left to right at the
bottom of the figure, parted magic 2019_01_03 Free Activators. The averaged spectrum of the selected, phase corrected
channels
(channels 1 and 2) is shown in FIG. 12, which reflects significant SNR
improvement versus
the uncorrected all channel average spectrum shown in FIG. 7.
[] Frame Editing
[] While it is contemplated that in hma vpn license key for android 2020 Activators Patch circumstances individual
MRS
acquisition frames may provide some useful information, frame averaging is
prevalently
indicated in the vast majority of cases to achieve a spectrum with sufficient
SNR and
interpretable signal at regions of interest for pathology assessment. It is,
at most, parted magic 2019_01_03 Free Activators, quite rare
that an individual frame will have sufficient SNR for even rudimentary
metabolite analysis to
the extent providing reliable diagnostic information. Often individual frames
along an
acquisition= series will have such low SNR, or possess such artifacts, that
they make no
improvement to the average - and in fact may even degrade it. To the extent
these "rogue"
frames may be recognized as such, they may be excluded from further processing
¨ with only
robust frames remaining, the result should improve.
[] Accordingly, a further mode of the present DDD-MRS processor
embodiment utilizes a frame editor to conduct frame editing to identify those
frames which


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vary sufficiently from the expected or otherwise observed acquisition results
such that they
should be excluded, as is also represented schematically in the flow diagram
examples of
FIGS, parted magic 2019_01_03 Free Activators. 9A-B. In one aspect of the underlying concern, certain patient motions
during an
acquisition may result in signal drop-out as well as frequency shifts (e.g.
magnetic
susceptibility artifact). While involuntary motion, e.g. respiration, is a
common cause of
frequency shifts, these are typically sufficiently minor and within a range
that they are not
believed to implicate signal quality other than the shift itself (which can be
significant source
of SNR degradation, but correctable per the present disclosure). However,
other more
significant movements (e.g. voluntary) may cause sufficiently significant
shifts to seriously
degrade the acquired spectrum, beyond merely correctable spectral shifts. For
example, such
activity may move the voxelated region to include adjacent tissues versus only
the intended
VOI upon prescription prior to the motion. If the salient artifact is
frequency shift, a
correction may be applied and the frame can be used to make a positive
contribution to the
averaged spectrum. If a frame is discarded its contribution is lost, and
across sufficient
number of discarded frames across a series the result may not include a
sufficient number of
frames in the average for a reliable SNR in the resulting spectrum, parted magic 2019_01_03 Free Activators. The DDD-
MRS
processor, according to the current embodiment, analyzes the residual water
signal in each
frame to determine if it is of sufficient quality to support frequency
correction.
[] FIG. 13 shows a time-intensity plot which illustrates a scan
series with
frequency shifts and "drop outs" with SNR changes considered to represent
corrupted frames
due to patient motion. More specifically, this shows 1 dimensional horizontal
lines for each
frame, with signal amplitude reflected in "brightness" or intensity (e.g.
higher values are
whiter, lower are darker), with time across the serial acquisition of the
series progressing top
to bottom vertically in the Figure. A vertical band of brightness is revealed
to the left side of
the plot. However, parted magic 2019_01_03 Free Activators, in this particular example, there is a clear break in this
band as "drop out"
frames. After excluding the "drop out" frames (center of time sequence between
about 75
and MRS frames, it was still possible to obtain a high quality final
averaged spectrum
from this scan using the remaining robust frames, as further developed
immediately below.
[] FIGS. 14A and 14B show the confidence level estimate and the
frame by
frame frequency error estimate, respectively, which are used according to the
present
embodiment for frame editing according to this acquisition series example of
FIG. More


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specifically, parted magic 2019_01_03 Free Activators, FIG. 14A shows the frame by frame confidence level, with
confidence level on
the Y-axis, parted magic 2019_01_03 Free Activators, and the sequential series of frame acquisitions along a scan
indicated along the X-
axis. FIG. 14B shows the actual frequency error along the Y-axis, for the same
frame series
along the X-axis. This is based on analyzing the characteristics of the
residual water peak
and the noise in a band 80 Hz wide (for 3T processing, the band would be 40 Hz
wide at
T) around parted magic 2019_01_03 Free Activators center-tuned frequency. The largest peak is assumed to be the
water signal
and the assumption is qualified by the confidence estimate. For the purpose of
this example,
if the confidence value is above a threshold, i.e.the frame is flagged
as a candidate macos wavebox
frequency correction and thus "retained." As seen from the plots in FIGS, parted magic 2019_01_03 Free Activators. 14A-
B, when the
confidence is low, the variance of the frequency error estimate is greatly
increased. The final
qualification step, per this example, is to determine if there are enough
qualified candidate
frames to achieve parted magic 2019_01_03 Free Activators SNR improvement when averaged. This threshold
limit for
proceeding with frequency correction (and thus frame editing therefore) has
been empirically
established as 90 frames meeting the criteria. According to the present
embodiments, this
has been observed to provide sufficiently robust results per the Examples
described herein. It
is to be appreciated, however, that other limits may be appropriate in various
circumstances. parted magic 2019_01_03 Free Activators number of frames required will be based upon the SNR levels achievable
from the
completed signal processing. This will be paced by SNR of input signal
acquisitions to begin
with, and performance of other signal processing modes and steps taken with
those signals.
According to the acquisitions under the Examples disclosed herein, SNR is
believed to
increase over about frames, and then with little gained typically
thereafter, though the 90
frame minimum limit has been observed to provide sufficient results when
reached (in rare
circumstances). In the event the result drops below the 90 frame limit, the
DDD-MRS
processor is still configured to proceed with other modes of signal
processing, signal quality
evaluation, and then diagnostic processor may be still employed ¨ just without
the added
benefit of the frame editing and frequency error correction.
[] Further description related to acceptable confidence level
estimate
approach according to the present disclosure is provided as follows, for
further illustration of
this embodiment for the frame editing and frequency correction modes of the
disclosure. The
discrete amplitude parted magic 2019_01_03 Free Activators can be analyzed in the range of the center-tuned
frequency 40
Hz for example in the case of a 3T system acquisition, parted magic 2019_01_03 Free Activators, and half this bounded
range (e.g. 20


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Hz) for a T system acquisition. The highest peak is located to determine
its width at the
half-amplitude point. Next, the total spectral width of all parts of the
spectrum which exceed
the half-amplitude point of the highest peak are determined. The confidence
estimate is
formed by taking the ratio of the spectral width of the greatest peak divided
by the total
spectral width which exceeds the threshold. If there is only a single peak
above the
threshold, the confidence estimate will beadobe character animator cc 2019 crack download Activators Patch there are many other peaks
or spectral
components which could be confused with the greatest one, then the estimate
will approach
0Ø This provides a simple and robust estimate of the randomness or dispersal
of energy in
the vicinity of the water peak. Like another approach using entropy
measurement, e.g. as
described below, this current approach provides at least one desirable
characteristic in that it's
performance is substantially invariant with amplitude.
[] Yet another system and method to compute a confidence estimate
that also
can be appropriate is provided as follows. The spectral entropy is computed by
normalizing
the spectrum to take the form of a probability mass function. The Shannon
entropy or
uncertainty function, H, is then computed as follows:
H = -Epi log, pi
where p = probability, and i = frequency index value (e.g. to +40 hz).
[] It is to be appreciated that other approaches to quantify
randomness or
uncertainty of the spectrum may also be suitable for use with the various
DDD_MRS signal
processor aspects of the present disclosure.
[] For further understanding and clarity re: the ultimate impact
frame editing
as described herein, the unprocessed absorption spectrum plot for all six
channels from the
patient (with the compromised frames included as aggregated in the respective
channel
spectra) in various views in prior Figures is shown for each respective
channel in the six
indicated panes shown in FIG. The phase and frequency corrected spectrum
averaged for
selected channels 3 and 4, and for all acquired frames aggregated/averaged
per channel,
without applying frame editing and thus including the corrupted frames, is
shown in FIG.
16A. In contrast, FIG. 16B shows a similar phase and frequency error corrected
spectrum
averaged for the same selected channels 3 and 4, but for only of the
acquired frames
aggregated/averaged per channel (the remaining frames edited out), per
frame editing
applied according to the present embodiments prior to frequency error
correction. The peak


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value in the combined lactate-alanine (LAAL) region of the frame edited
spectrum of FIG, parted magic 2019_01_03 Free Activators.
16B is significantly increased ¨ with corresponding increase in SNR ¨ relative
to the peak
value in the same LAAL region of the non-frame edited spectrum in FIG. 16A
(e.g. the peak
value increases from about x to about x ), a nearly 20% SNR
increase despite
about 40% corresponding reduction in the number of FID frames used.
[] While the examples addressed above by reference to FIGS. B
address a highly beneficial embodiment for frame editing based upon water
signal, other
frame editing embodiments are also contemplated, and many different features
of acquired
DDD-MRS signals may be used for this purpose. One such further embodiment is
shown for
example by reference to another DDD-MRS pulse sequence acquisition for another
disc in
another subject by reference to FIGS, parted magic 2019_01_03 Free Activators. 17A-F. More specifically, per the time-
intensity plot
shown for this acquisition in FIG. 17A, while the water signal region of the
acquired spectral
series (bright vertical band on left side of plot) reveals some shift
artifact, another bright band
appears at a broader region on the right side of the spectra, between about
and FID
frames into the exam. This region is associated with lipid, and also overlaps
with lactic acid
(LA) and alanine (AL) regions of diagnostic interest according to the present
detailed
embodiments and Examples. This is further reflected in FIG. 17B which shows a
waterfall
plot of running cumulative average of acquired frames in series, where signal
amplitude rises
in this lipid-related spectral region during this portion of the exam. A
resulting average
spectral plot for channels 1 and 2 of this acquisition, post phase and
frequency correction
(again noting water signal did not prompt frame editing to remove many frames)
is shown for
reference in FIG. 17C, parted magic 2019_01_03 Free Activators. This resulting spectrum has significant signal peak
intensity and line
width commensurate with lipid signal, and which shrouds an ability to assess
underlying LA
and/or AL chemicals overlapping therewith in their respective regions.
Accordingly, an
ability to measure LA and AL being compromised may also compromise an ability
to make a
diagnostic assessment of tissue based upon these chemicals (as if un
compromised by
overlapping lipid). However, as this lipid contribution clearly only occurs
mid-scan, an
ability to edit it out to assess signal without that portion of the exam may
provide a robust
result for LA and AL-based evaluation nonetheless.
[] This is shown in FIGS. 17D-F where only the first frames of
the same
acquisition are evaluated, which occur prior to the lipid contribution arising
in the acquired


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signals. As is shown here, no lipid signal is revealed in the parted magic 2019_01_03 Free Activators intensity
plot of FIG. 17D,
or waterfall plot of FIG. 17E, or resulting final spectrum of FIG. parted magic 2019_01_03 Free Activators, though
strong
proteoglycan peak is shown with very little (if any) LA or AL in the signal of
otherwise high
SNR (e.g. per the PG peak). As this example illustrates a DDD-MRS processed
acquisition
for a non-painful control disc, strong PG signal and little to no LA and/or AL
signal is
typically expected, and this thus represents a diagnostically useful, robust
signal for intended
purpose (whereas the prior spectra without editing out the lipid frames may
have erroneously
biased the results). Accordingly, it is contemplated that a lipid editor may
also be employed
as a further embodiment for frame editing, with approaches for recognizing
lipid signal taken