U.S. patent application number 14/478875 was filed with the patent office on 2015-03-05 for system for magnetic resonance spectroscopy of brain tissue for pattern-based diagnostics.
This patent application is currently assigned to The United States of America, as represented by the Secretary, Dept. of Health and Human Services. The applicant listed for this patent is The United States of America, as represented by the Secretary, Dept. of Health and Human Services, The United States of America, as represented by the Secretary, Dept. of Health and Human Services. Invention is credited to Pierre Alusta, Richard D. Beger, Dan A. Buzatu, Inessa IM, Ryan M. Kretzer, Bruce Pearce, Jon G. Wilkes.
Application Number | 20150065853 14/478875 |
Document ID | / |
Family ID | 43992413 |
Filed Date | 2015-03-05 |
United States Patent
Application |
20150065853 |
Kind Code |
A1 |
Wilkes; Jon G. ; et
al. |
March 5, 2015 |
SYSTEM FOR MAGNETIC RESONANCE SPECTROSCOPY OF BRAIN TISSUE FOR
PATTERN-BASED DIAGNOSTICS
Abstract
A system and method for preprocessing magnetic resonance
spectroscopy (MRS) data of brain tissue for pattern-based
diagnostics is disclosed. The MRS preprocessing system includes an
MRS preprocessing module that executes an operation that normalizes
MRS spectrum data, recalibrates and scales the normalized MRS
spectrum data, and then renormalizes the scaled MRS spectrum data.
The resulting preprocessed MRS data is used to assist in
identifying abnormalities in tissues shown in MRS scans.
Inventors: |
Wilkes; Jon G.; (Little
Rock, AR) ; Buzatu; Dan A.; (Benton, AR) ;
Alusta; Pierre; (Little Rock, AR) ; Pearce;
Bruce; (White Hall, AR) ; Kretzer; Ryan M.;
(Baltimore, MD) ; IM; Inessa; (Little Rock,
AR) ; Beger; Richard D.; (White Hall, AR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The United States of America, as represented by the Secretary,
Dept. of Health and Human Services |
Rockville |
MD |
US |
|
|
Assignee: |
The United States of America, as
represented by the Secretary, Dept. of Health and Human
Services
Rockville
MD
|
Family ID: |
43992413 |
Appl. No.: |
14/478875 |
Filed: |
September 5, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13509539 |
Aug 6, 2012 |
8880354 |
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PCT/US2010/056486 |
Nov 12, 2010 |
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14478875 |
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61261170 |
Nov 13, 2009 |
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Current U.S.
Class: |
600/410 ;
324/322 |
Current CPC
Class: |
G01R 33/387 20130101;
A61B 5/055 20130101; G01R 33/465 20130101; G16H 50/20 20180101;
G01R 33/4625 20130101; A61B 5/0042 20130101; G16H 10/40
20180101 |
Class at
Publication: |
600/410 ;
324/322 |
International
Class: |
A61B 5/055 20060101
A61B005/055; G01R 33/387 20060101 G01R033/387; G01R 33/465 20060101
G01R033/465; A61B 5/00 20060101 A61B005/00; G01R 33/46 20060101
G01R033/46 |
Claims
1.-22. (canceled)
23. An MRS preprocessing system for preprocessing raw MRS spectrum
data comprising: one or more processors; and a plurality of modules
configured to be executed by the one or more processors, the
plurality of modules comprising: a variance-weighting module to
scale the raw MRS spectrum data by using a plurality of weighting
constants to generate a weighted MRS spectrum data, thereby
generating a preprocessed MRS spectrum data.
24. The system of claim 23, wherein each of the plurality of
weighting constants correspond to a particular nuclear magnetic
resonance frequency.
25. The system of claim 24, wherein scaling the raw MRS spectrum
data includes dividing each of the summary of signals at a
particular nuclear magnetic resonance frequency by one of the
plurality of weighting constants corresponding to that same
particular nuclear magnetic resonance frequency.
26. The system of claim 23, wherein each of the plurality of
weighting constants at a particular nuclear magnetic resonance
frequency represents a standard deviation from a mean value of the
raw MRS spectrum data associated with a single type of tissue at
the particular nuclear magnetic resonance frequency.
27. The system of claim 23, wherein each of the plurality of
weighting constants at a particular nuclear magnetic resonance
frequency represents an average standard deviation taken from an
average of raw MRS spectrum data associated with a plurality of
types of tissue at the particular nuclear magnetic resonance
frequency.
28. A non-transitory machine-readable media encoded with an MRS
preprocessing system to process raw MRS spectrum data, the MRS
preprocessing system comprising machine readable instructions
executable by at least one processor to perform the steps of:
receiving raw MRS spectrum data at the MRS preprocessing system
executing on at least one processor; scaling the raw MRS spectrum
data by using a plurality of weighting constants to generate a
weighted MRS spectrum data, thereby generating a preprocessed MRS
spectrum data.
29. The machine-readable media of claim 28, the raw MRS spectrum
data comprises a summary of signals produced by a tissue sample at
a range of nuclear magnetic resonance frequencies.
30. The machine-readable media of claim 29, wherein scaling the MRS
spectrum data using the plurality of weighting constants further
comprises enhancing the at least one of the summary of signals
being representative of a particular biomarker relative to the at
least another one of the summary of signals representative of
random noise.
31. The machine-readable media of claim 28, wherein each of the
plurality of weighting constants corresponds to a particular
nuclear magnetic resonance frequency.
32. The machine-readable media of claim 31, wherein scaling the MRS
spectrum data includes dividing each of the summary of signals at a
particular nuclear magnetic resonance frequency by one of the
plurality of weighting constants corresponding to that same
particular nuclear magnetic resonance frequency.
33. The machine-readable media of claim 29, wherein each of the
plurality of weighting constants at a particular nuclear magnetic
resonance frequency represents a standard deviation from a mean
value of the normalized MRS spectrum data associated with a single
type of tissue at the particular nuclear magnetic resonance
frequency.
34. The machine-readable media of claim 33, wherein each of the
plurality of weighting constants at a particular nuclear magnetic
resonance frequency represents an average standard deviation taken
from an average of raw MRS spectrum data associated with a
plurality of types of tissue at the particular nuclear magnetic
resonance frequency.
35. The MRS preprocessing system of claim 23, further comprising a
normalization module and a renormalization module, wherein the
normalization module normalizes the raw MRS spectrum data to
generate normalized MRS spectrum data, wherein the
variance-weighting module scales the normalized MRS spectrum data,
and wherein the renormalization module renormalizes the weighted
MRS spectrum data to generate a preprocessed MRS spectrum data.
36. The machine-readable media of claim 28, further comprising
normalization and renormalization steps, wherein the normalizing
step comprises normalizing the raw MRS spectrum data to generate
normalized MRS spectrum data, wherein the scaling step comprises
scaling the normalized MRS spectrum data, and wherein renormalizing
step comprises renormalizing the weighted MRS spectrum data to
generate a preprocessed MRS spectrum data.
37.-50. (canceled)
51. A method for detecting a tissue abnormality comprising:
providing to at least one computing device a preprocessed MRS
spectrum data of a tissue sample comprising a summary of signals
produced by the tissue sample at a range of nuclear magnetic
resonance frequencies, wherein the preprocessed MRS spectrum data
of the tissue sample further comprises a raw MRS spectrum data
subjected to a preprocessing method including scaling by
variance-weighting; providing to the at least one computing device
a set of preprocessed comparison MRS spectrum data, comprising a
plurality of preprocessed comparison MRS spectrum data, wherein
each preprocessed comparison MRS spectrum data of the set comprises
a summary of signals produced by an abnormal tissue sample having a
known abnormality at a range of nuclear magnetic resonance
frequencies, and wherein each preprocessed comparison MRS spectrum
data of the set further comprises a raw comparison MRS spectrum
data subjected to a preprocessing method including scaling by
variance-weighting; comparing, by the at least one computing
device, the preprocessed MRS spectrum data of the tissue sample to
the set of preprocessed comparison MRS spectrum data using a
pattern recognition method; and, identifying the tissue abnormality
as the known abnormality of the preprocessed comparison MRS
spectrum data that most closely matches the preprocessed MRS
spectrum data of the tissue sample.
52. (canceled)
53. The method of claim 51, wherein the known abnormality comprises
normal tissue, one or more malignant tumor tissues, one or more
benign tumor tissues, or one or more non-cancerous tissue
abnormalities.
54. The method of claim 51, wherein the a raw MRS spectrum data and
the raw comparison MRS spectrum data are subjected to a
preprocessing method further comprising normalization and
renormalization.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application No. 61/261,170, filed on Nov. 13, 2009.
FIELD
[0002] This document relates to magnetic resonance spectroscopy
("MRS"), and in particular a system and method for pre-processing
MRS data of brain tissue for pattern-based diagnostics.
BACKGROUND
[0003] The diagnosis of brain tissue anomalies is an ongoing
challenge in modern medicine. Because of the location of the brain
within the skull, and the sensitivity of brain tissue to invasive
procedures, the diagnosis of suspected brain disease balances the
need for timely and accurate diagnosis with the need to minimize
damage to the brain tissue in the course of performing diagnostic
procedures. The risk of adverse outcomes related to the diagnostic
procedures is significant. For example, in 7,500 brain biopsy
procedures conducted from 1979 to 1991, the diagnostic accuracy was
91%. However, the morbidity and mortality rates associated with the
invasive biopsy procedure were 3.5% and 0.7%, respectively.
[0004] In addition, early detection leading to early intervention
in a brain anomaly is an important consideration. However, due to
the dangers associated with biopsy in a situation of recognized
tissue anomaly, early detection is typically achieved using
non-invasive procedures such as computed tomography (CT) or
magnetic resonance imaging (MRI). However, existing non-invasive
diagnostic procedures are relatively ineffective at detecting the
early stages of a brain anomaly. Based on non-invasive procedures
such as CT or MRI scans alone, practitioners cannot consistently
distinguish radiation necrosis or benign lesions from malignant
tumors.
[0005] Magnetic resonance spectroscopy (MRS) with pattern
recognition has recently shown potential for the non-invasive
diagnosis of brain lesions, for direction of surgical or other
therapeutic interventions, and for determining prognosis. Patterns
indicative of abnormal tissue appear in brain MRS scans before any
abnormality is indicated in the corresponding MRI scans. Therefore,
use of reliable MRS-based patterns could enable earlier detection
of brain tissue anomalies. Further, if the MRS scans contain
sufficiently distinctive and reliable markers, MRS diagnostic
procedures could augment or substitute for histological grading,
guide surgical intervention at tumor margins and areas of local
invasion, and monitor radiation or chemo-therapy progress. Whether
these potential advantages are realized depends on the predictive
quality of computational models based on MRS scan data that in turn
depend on the consistency and inherent informational content of the
MRS scans.
[0006] Previous research on a variety of tissue types indicates
that 1.5 Tesla (T) MRS scans contain information useful for
diagnostic purposes, particularly biomarkers for AT-acetyl
aspartate (NAA), choline (Cho), creatinine (Cr), myo-inositol (MI),
lactate, and lipids. For example, using an automated two-category
classification model, MRS diagnostic procedures achieved a
sensitivity of 80% and specificity of 86% for discriminating breast
cancer tissue from scar tissue. However, in brain or other tissues,
simple biomarker ratios used for diagnosis such as Cho/Cr cannot
adequately distinguish malignant lesions from other features such
as progressive multifocal leukoencephalopathy, multiple sclerosis,
stroke, and scar tissue.
[0007] The detection of brain lesions using MRS requires
recognizing patterns in MRS scans associated with abnormalities.
One method involves the interpretation of MRS scan patterns by an
expert practitioner. When an expert interprets MRS spectra for
diagnostic purposes, he/she may consider the relative amounts or
ratios of two or three biomarkers such as Cho/Cr. Spectrum scans
are visually examined, peak biomarkers are identified, and peak
heights are scaled manually to determine these ratios. MRS peaks,
even if not precisely at the expected chemical shift, may be
recognized for the biomarker they represent by the relationship of
the biomarker peak to the familiar pattern of peaks in the MRS
scan.
[0008] Diagnostic pattern recognition on the whole MRS spectrum,
rather than classification based on a few biomarker ratios, is a
recent development. The effective use of multiple biomarkers for
diagnosis requires not expert, but automated peak identification
and quantification. For example, a multicenter study evaluated
automated classification of tumors based on proton MRS patterns
using linear discriminant analysis and leave-one-out validation to
achieve greater than 90% correct classification of the tissue type
in scans from multiple instruments and centers. However, the
classification scheme of this study was limited to only three types
of tumors. In routine practice, the accurate diagnosis of many
different types of tumors as well as normal tissue and other types
of non-cancerous anomalies is highly desirable. This goal may be
achieved using MRS scans of normal tissue and other types of
non-cancerous anomalies in the training and external validation
sets of an automated diagnostic pattern recognition method. The
accuracy and utility of predictive models strongly depend on at
least several factors related to the quality of the MRS scans such
as the reproducibility of chemical shifts for each biomarker and
the potential for confusion among the spectrum classes modeled.
[0009] For example, spatial variation in B.sub.o, the magnetic
field strength of the MRS devices, may result in the misalignment
of biomarker chemical shifts. The magnetic field strength varies as
a function of target tissue depth. Although the scale of the
variations in chemical shift is not so great as to cause
misidentification of markers under expert interpretation, the same
is not true when automated detection of many different biomarkers
is required for computerized pattern recognition. Misidentification
of biomarkers between data replicates confounds the MRS-based
models and effectively decreases peak resolution by spreading the
domain over which each diagnostic feature may appear. For example,
FIG. 1 shows an example of the variation in biomarker chemical
shifts among several spectra measured from the same tissue
type.
[0010] The non-uniformity of biomarker peaks represents a
significant limitation in use of automated MRS-based pattern
recognition. High-magnetic-field medical MRI devices produce MRS
scans that have greater biomarker peak resolution and therefore
superior ability to distinguish proximate, but diagnostically
distinct, spectrum features. For example, using a high-powered 8.5T
MRS device to analyze tissue samples from fine needle biopsies,
breast tissue anomalies were detected with nearly 100% correct
categorization and sensitivity for the extent of breast tissue
anomalies. However, the high-powered MRS instruments are not nearly
as ubiquitous as 1.5T MRS instruments in the medical community.
[0011] Another approach to enhancing the quality of MRS data is the
preprocessing of the raw MRS data prior to analysis. Data
preprocessing is a well-known tool used in research fields that
utilize high volumes of data containing both signal and noise, such
as mass spectrometry, genomics, proteomics, metabolomics, and
structure-activity relationships. Common preprocessing techniques
typically include normalization, baseline correction, various kinds
of weighting, smoothing, variance or other kinds of scaling, and a
priori information weighting. In all computational modeling, such
as that used for MRS pattern recognition, there is a concern that
preprocessing may obscure the informational content of the raw
data; validation of the model and its predictions is important in
order to assess the value of preprocessing steps.
[0012] Previous research has highlighted the beneficial effects of
preprocessing MRS data using normalization and digitization on the
diagnostic outcome of predictive diagnostic models. However,
research to date has overlooked the importance of re-calibrating
MRS spectra to account for B.sub.o variations in order to realign
the chemical shifts as a critical first step in data preprocessing.
A need exists in the art for a preprocessing method that minimizes
variation in chemical shifts and enhances the resolution of
biomarker shifts relative to random noise. Due to the higher
consistency of the preprocessed MRS relative to the raw MRS scans,
higher resolution of tissue types including healthy tissue,
non-malignant tumors, and different types of tumors could be
achieved. Further, the higher fidelity signal even when the MRS
signal is relatively weak resulting from preprocessing would make
possible the earlier detection of tissue anomalies. In addition,
preprocessing of MRS data measured using the more commonly
available lower-powered MRS devices would make the enhanced
diagnostic methodologies using automated MRS pattern detection more
widely available.
SUMMARY
[0013] In an embodiment, a method for preprocessing Magnetic
Resonance Spectroscopy (MRS) spectrum data may include the steps of
providing a raw MRS spectrum data and scaling the raw MRS spectrum
data by using a plurality of weighting constants to generate a
preprocessed MRS spectrum data.
[0014] In another embodiment, a method for preprocessing Magnetic
Resonance Spectroscopy (MRS) spectrum data may include the steps
of: providing a database including raw MRS spectrum data, defined
herein as a frequency-domain spectrum resulting from a Fourier
transform of a set of free induction decay (FID) data collected by
the MRS instrument for a particular tissue sample. The method may
further include normalizing the raw spectrum data to generate
normalized MRS spectrum data, scaling the normalized MRS spectrum
data by using a plurality of weighting constants to generate a
weighted MRS spectrum data, and renormalizing the weighted MRS
spectrum data to generate a preprocessed MRS spectrum data.
[0015] In an embodiment, a method for preprocessing MRS spectrum
data includes providing a raw MRS spectrum data, recalibrating the
raw MRS spectrum data, and scaling the recalibrated MRS spectrum
data by using a plurality of weighting constants to generate a
preprocessed MRS spectrum data.
[0016] In yet another embodiment, a method for preprocessing MRS
spectrum data may include the steps of: providing a database having
raw MRS spectrum data, normalizing the raw spectrum data to
generate normalized MRS spectrum data, scaling the normalized MRS
spectrum data by using a plurality of weighting constants to
generate a weighted MRS spectrum data, and renormalizing the
weighted MRS spectrum data to generate a preprocessed MRS spectrum
data.
[0017] In one embodiment, an MRS preprocessing system for
preprocessing raw MRS spectrum data may include one or more
processors and a plurality of modules configured to be executed by
the one or more processors. The plurality of modules may include a
normalization module to normalize the raw MRS spectrum data to
generate normalized MRS spectrum data. In addition, a recalibration
module shifts each of the nuclear magnetic resonance frequencies
representing a particular biomarker to a reference nuclear magnetic
resonance frequency representing the particular biomarker to
generate a recalibrated MRS spectrum data. A variance-weighting
module scales the recalibrated MRS spectrum data by using a
plurality of weighting constants to generate a weighted MRS
spectrum data. Finally, a renormalization module renormalizes the
weighted MRS spectrum data to generate a preprocessed MRS spectrum
data.
[0018] In yet another embodiment, a machine-readable media may be
encoded with an MRS preprocessing system to process raw MRS
spectrum data. The MRS preprocessing system may include machine
readable instructions executable by at least one processor to
perform the steps of: receiving raw MRS spectrum data at the MRS
preprocessing system executing on at least one processor,
normalizing the raw MRS spectrum data to generate normalized MRS
spectrum data, scaling the normalized MRS spectrum data by using a
plurality of weighting constants to generate a weighted MRS
spectrum data, and renormalizing the weighted MRS spectrum data to
generate a preprocessed MRS spectrum data.
[0019] In a further embodiment, a machine-readable media may be
encoded with an MRS preprocessing system to process raw MRS
spectrum data, the MRS preprocessing system comprising machine
readable instructions executable by at least one processor to
perform the steps of receiving raw MRS spectrum data at the MRS
preprocessing system executing on at least one processor;
normalizing the raw MRS spectrum data to generate normalized MRS
spectrum data; recalibrating the normalized MRS spectrum data;
scaling the recalibrated MRS spectrum data by using a plurality of
weighting constants to generate a weighted MRS spectrum data; and
renormalizing the weighted MRS spectrum data to generate a
preprocessed MRS spectrum data.
[0020] In one embodiment, a method for detecting tissue
abnormalities may include the steps of: providing a database
including a raw MRS spectrum data, normalizing the raw spectrum
data to generate normalized MRS spectrum data, scaling the
normalized MRS spectrum data by using a plurality of weighting
constants to generate a weighted MRS spectrum data; and
renormalizing the weighted MRS spectrum data to generate a
preprocessed MRS spectrum data, whereby the preprocessed MRS
spectrum data provides a means for detecting tissue abnormalities
for pattern recognition diagnostics of a tissue at an accuracy rate
of at least 90%.
[0021] A method for preprocessing MRS spectrum data may include
providing a database having a raw MRS spectrum data. The raw
spectrum data includes a summary of the signals produced by a
tissue sample at a range of nuclear magnetic resonance frequencies
with each of the raw spectrum data including one or more Nuclear
Magnetic Resonance (NMR) frequencies and corresponding signals
measured from the tissue at each NMR frequency, wherein each NMR
frequency is provided in the form of a chemical shift, the chemical
shift being the percentage shift in a particular NMR frequency
relative to the NMR frequency of a reference chemical. The raw
spectrum data is normalized to generate normalized MRS spectrum
data, and then the normalized MRS spectrum data is scaled using a
plurality of weighting constants to generate a weighted MRS
spectrum data. Finally, the weighted MRS spectrum data is
renormalized to generate a preprocessed MRS spectrum data with the
preprocessed MRS spectrum data having a value range from a minimum
value of 0 to a maximum value of 1.
[0022] In one embodiment, a method for detecting a tissue
abnormality may include providing a preprocessed MRS spectrum data
of a tissue sample made up of a summary of signals produced by the
tissue sample at a range of nuclear magnetic resonance frequencies.
In addition the preprocessed MRS spectrum data of the tissue sample
may include a raw MRS spectrum data subjected to a preprocessing
method including normalization, recalibration, scaling by
variance-weighting, and renormalization. The method also may
include providing a set of preprocessed comparison MRS spectrum
data that may include a plurality of preprocessed comparison MRS
spectrum data, where each preprocessed comparison MRS spectrum data
of the set may include a summary of signals produced by an abnormal
tissue sample having a known abnormality at a range of nuclear
magnetic resonance frequencies. Further, each preprocessed
comparison MRS spectrum data of the set may include a raw
comparison MRS spectrum data subjected to a preprocessing method
including normalization, recalibration, scaling by
variance-weighting, and renormalization. The method may also
include comparing the preprocessed MRS spectrum data of the tissue
sample to the set of preprocessed comparison MRS spectrum data
using a pattern recognition method and identifying the tissue
abnormality as the known abnormality of the preprocessed comparison
MRS spectrum data that most closely matches the preprocessed MRS
spectrum data of the tissue sample.
[0023] In another embodiment, a method for detecting a tissue
abnormality may include providing a preprocessed MRS spectrum data
of a tissue sample made up of a summary of signals produced by the
tissue sample at a range of nuclear magnetic resonance frequencies.
In addition the preprocessed MRS spectrum data of the tissue sample
may include a raw MRS spectrum data subjected to a preprocessing
method including normalization, recalibration, scaling by
variance-weighting, and renormalization. The method also may
include providing a set of preprocessed comparison MRS spectrum
data that may include a plurality of preprocessed comparison MRS
spectra data, where each preprocessed comparison MRS spectrum data
of the set may include a summary of signals produced by an abnormal
tissue sample having a known abnormality at a range of nuclear
magnetic resonance frequencies. Further, each preprocessed
comparison MRS spectrum data of the set may include a raw
comparison MRS spectrum data subjected to a preprocessing method
including normalization, recalibration, scaling by
variance-weighting, and renormalization. The method may further
include forming a tissue biomarker signal group that may include at
least one of the summary of signals produced by the tissue sample
at a particular nuclear magnetic resonance frequency representing a
particular biomarker and forming a set of comparison tissue
biomarker signal groups, where each comparison tissue biomarker
signal group of the set may include at least one of the summary of
signals produced by an abnormal tissue sample having a known
abnormality at a particular nuclear magnetic resonance frequency
representing the same particular biomarker as the particular
biomarker of the tissue biomarker signal group. The method may also
include comparing the tissue biomarker signal group to the set of
comparison tissue biomarker signal groups using a pattern
recognition method and identifying the tissue abnormality as the
known abnormality of the comparison tissue biomarker signal group
most closely matching the tissue biomarker signal group of the
tissue sample.
[0024] Additional objectives, advantages and novel features will be
set forth in the description which follows or will become apparent
to those skilled in the art upon examination of the drawings and
detailed description which follows.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] FIG. 1 is a graph of the MRS spectra of 24 oligodendroglioma
tissue samples;
[0026] FIG. 2 is a block diagram illustrating a non-limiting
exemplary embodiment of an MRS preprocessing system;
[0027] FIG. 3 is a block diagram illustrating a non-limiting
exemplary embodiment of an MRS preprocessing system;
[0028] FIG. 4 is a flow chart of the processes of a normalization
module in a non-limiting exemplary embodiment of an MRS
preprocessing system;
[0029] FIG. 5 is a flow chart of the processes of a chemical shift
recalibration module in a non-limiting exemplary embodiment of an
MRS preprocessing system;
[0030] FIG. 6 is a graph showing an MRS spectrum of astrocytoma
tissue overlaid with previously reported biomarker locations;
[0031] FIG. 7A is a graph comparing fourteen MRS spectra of
astrocytoma tissues prior to preprocessing;
[0032] FIG. 7B is a graph comparing fourteen MRS spectra of
astrocytoma tissues after chemical shift recalibration;
[0033] FIG. 8 is a flow chart of the processes of a
variance-weighting module in a non-limiting exemplary embodiment of
an MRS preprocessing system;
[0034] FIG. 9 is an exemplary weighting function in a non-limiting
exemplary embodiment of an MRS preprocessing system;
[0035] FIG. 10 is a flow chart of the processes of a
renormalization module in a non-limiting exemplary embodiment of an
MRS preprocessing system;
[0036] FIG. 11 is a block diagram of an MRS preprocessing system in
a non-limiting exemplary embodiment of an MRS preprocessing
system;
[0037] FIG. 12 is a block diagram of an MRS preprocessing system in
a non-limiting exemplary embodiment of an MRS preprocessing
system;
[0038] FIG. 13 is a block diagram of an MRS preprocessing system in
a non-limiting exemplary embodiment of an MRS preprocessing
system;
[0039] FIG. 14 is a graph summarizing the results of a principal
components analysis of normalized, but otherwise not preprocessed
MRS spectra;
[0040] FIG. 15 is a graph summarizing the results of a principal
components analysis of preprocessed MRS spectra; and
[0041] FIG. 16 is a graph summarizing the results of a principal
components analysis of preprocessed MRS spectra that included
external validation spectra.
[0042] Corresponding reference characters indicate corresponding
elements among the view of the drawings. The headings used in the
figures should not be interpreted to limit the scope of the
claims.
DETAILED DESCRIPTION
[0043] A Magnetic Resonance Spectroscopy (MRS) preprocessing system
is provided that processes MRS spectrum data that includes an array
of chemical shift values and corresponding signal values. The
preprocessed MRS spectrum data may be subsequently analyzed using
techniques including but not limited to visual inspection by an
expert practitioner and automated pattern recognition diagnostic
systems. Because the MRS preprocessing system compensates for the
confounding effects including but not limited to magnetic field
heterogeneity in the MRS device and random noise, a much higher
efficacy of automated pattern recognition diagnostic methods may be
achieved. The preprocessing methods implemented by the MRS
preprocessing system may include a chemical shift recalibration in
which the chemical shifts in the MRS spectrum data may be adjusted
based on the alignment of selected chemical shifts in the MRS
spectrum data to coincide with the previously determined chemical
shifts of known biomarkers. Another method that may be optionally
implemented by the MRS preprocessing system is a variance-weighting
method, in which the signal values at chemical shifts corresponding
to frequently observed biomarker peaks may be enhanced relative to
the signal values at chemical shifts corresponding to random noise,
n which relatively few biomarker peaks are observed.
[0044] Referring to the drawings, a non-limiting exemplary
embodiment of an MRS preprocessing system is illustrated and
generally indicated as 100 in FIGS. 2-13. MRS spectrum data may
include one or more signals paired with a corresponding NMR
frequency. The signal may correspond to an amount of energy
released by charged particles including but not limited to protons
in a tissue sample in response to the absorption of applied
electromagnetic pulses. The NMR frequency is defined herein as the
resonant frequency of an applied electromagnetic pulse used to
generate a signal. The various embodiments of the MRS preprocessing
system 100 may provide a means for preprocessing raw MRS spectrum
data 101 for the detection of tissue abnormalities using methods
including but not limited to pattern recognition diagnostics of
tissue. Raw MRS spectrum data 101, as defined herein, is a summary
of the signals produced by a tissue sample at a range of different
nuclear magnetic resonance frequencies. Each of the spectra in the
raw MRS spectra data 101 may include one or more NMR frequencies
and the corresponding signals measured from the sample at each NMR
frequency. The NMR frequency may be provided in the form of a
chemical shift, defined herein as the percentage shift in NMR
frequency relative to the NMR frequency of a reference chemical,
expressed in parts per million (ppm). In the context of this
application, the terms "chemical shift" and "NMR frequency" may be
used interchangeably. Biomarkers contained in the sample typically
produce a strong signal at a characteristic chemical shift, thereby
may result in an identifiable signal peak at the biomarker's
characteristic chemical shift.
[0045] The preprocessed MRS spectrum data 103 resulting from the
processing of the raw MRS spectrum data 101 by embodiments of the
MRS preprocessing system 100 may be used to diagnose tissue
abnormalities by detecting patterns in the chemical shifts of the
MRS spectrum. The chemical shift patterns may be detected by
implementing an automated pattern detection method by the MRS
preprocessing system 100. Because the preprocessed MRS spectrum
data 103 may be of a more uniform quality than the raw MRS spectrum
data 101, the diagnosis accuracy resulting from preprocessed MRS
data 103 may be significantly enhanced relative to the diagnosis
accuracy resulting from unprocessed MRS data 101 in various
embodiments.
MRS Preprocessing System Overview
[0046] Referring to FIG. 2, a non-limiting exemplary embodiment of
the MRS preprocessing system 100 may use a combination of chemical
shifts and variance weighting for preprocessing raw MRS spectrum
data 101 that may be communicated to the MRS processing system 100
by input system 102 or otherwise stored or present in the MRS
preprocessing system 100. Input system 102 may include one or more
devices or systems used to generate or transfer an electronic
version of one or more raw MRS spectrum data 101 to the MRS
preprocessing system 100. Once the raw MRS spectrum data 101 is
preprocessed by the MRS processing system 100, the preprocessed
spectrum data 103 may be passed to an output system 106 for storage
and later analysis using a technique including but not limited to
known automated pattern recognition diagnostic systems.
Alternately, the preprocessed spectra data 103 may stored at the
MRS preprocessing system 100. The MRS preprocessing system 100 may
also generate information to the user interface 108, including but
not limited to the status of the preprocessing process, results, or
queries for system input.
[0047] In one non-limiting embodiment, the MRS preprocessing system
100 may include one or more processors 112 that may be embodied by
or in one or more distributed or integrated components or systems.
The MRS preprocessing system 100 may include a database 110 on
which data may be stored and a computer readable media (CRM) 104 on
which one or more algorithms, software, modules, data, computer
readable instructions, and/or firmware may be loaded and/or
operated and/or which may operate on the one or more processors 112
to implement the systems and methods identified herein. In an
embodiment, the database may be a storage system that temporarily
and/or permanently stores data and may include volatile and/or
nonvolatile memory and/or other types of storage systems or
devices.
MRS Preprocessing System
[0048] Referring to FIG. 3, a block diagram illustrates the modules
of a non-limiting exemplary embodiment of the MRS preprocessing
system 100 that may execute on the processor 112 to preprocess the
raw MRS spectrum data 101. The MRS preprocessing system 100 may
include modules including but not limited to a normalization module
202 for normalizing raw MRS spectrum data 101, a chemical shift
recalibration module 204 for recalibrating the normalized MRS
spectrum data 210, a variance-weighting module 206 for providing a
scaling function and scaling the recalibrated MRS spectrum data
212, and a renormalization module 208 that may optionally
renormalize the weighted spectrum data 214 as shall be discussed in
greater detail below.
[0049] The normalization module 202 may process the raw MRS
spectrum data 101 and normalize the raw MRS spectrum data 101 such
that the signal values at all chemical shifts of the normalized MRS
spectrum data 210 may be normalized to values ranging from a
normalized minimum value to a normalized maximum value. The
chemical shift recalibration module 204 may then recalibrate the
normalized MRS spectrum data 210 by re-aligning the chemical shifts
corresponding to two or more selected biomarkers to standardized
chemical shift values, and may optionally interpolate all remaining
chemical shifts in the normalized MRS spectrum data 210 to fall in
an even distribution between the two or more re-aligned chemical
shifts. The resulting recalibrated MRS spectrum data 212 may be
processed by the variance-weighting module 206, which may scale the
data 212 at each chemical shift up or down according to a
predetermined schedule based on the observed variance of the
signals in a set of reference MRS spectra analyzed previously. The
variance-weighted MRS spectrum data 214 may optionally be further
processed by the renormalization module 208, which may renormalizes
the data 214 such that the signal values at all chemical shifts of
the renormalized MRS spectrum data 215 range from a normalized
minimum value to a normalized maximum value. The renormalized MRS
spectrum data 215 may be passed to the output system 106 for
analysis by techniques including but not limited to visual
inspection by an expert practitioner or automated pattern
recognition techniques.
Normalization Module
[0050] Referring to FIG. 4, a flow chart illustrates the operation
of a non-limiting exemplary embodiment of a normalization module
202. The normalization module 202 may process raw MRS spectrum data
101, which may include a summary of at least one signal, a minimum
signal value, and a maximum signal value. The normalization module
202 may transform all signal values of the raw spectrum data to
values ranging from a normalized minimum value to a normalized
maximum value range. One non-limiting example of a normalized
minimum value is 0, and one non-limiting example of a normalized
maximum value is 1. At step 302, the normalization module 202 may
access the raw MRS spectrum data 101, which may be stored on the
input system 102. In this non-limiting exemplary embodiment, the
normalization module 202 may determine a minimum signal value of
all the signal values in the spectrum at step 304 and may subtract
the minimum signal value from all signal values in the MRS spectrum
at step 306 to generate a shifted signal. In this embodiment, the
shifted signal values of the MRS spectrum may range between a
signal value of 0 at the same chemical shift at which the minimum
value was determined in step 304 and a maximum signal value. This
maximum signal value may be determined in step 308, and all shifted
signal values may be divided by this maximum signal value at step
310 to produce the normalized MRS spectrum data 210.
Chemical Shift Recalibration Module
[0051] FIG. 5 is a flow chart illustrating the operation of one
non-limiting exemplary embodiment of the chemical shift
recalibration module 204. The chemical shift recalibration module
204 may recalibrate the normalized MRS spectrum data 210 by
shifting each of the nuclear magnetic resonance frequencies
representing a particular biomarker to a reference nuclear magnetic
resonance frequency representing the same particular biomarker. At
step 402, the chemical shift recalibration module 204 may process
the normalized MRS spectrum data 210 and corrects the chemical
shifts of the data 210 to compensate for magnetic field
non-homogeneity in the MRS apparatus (not shown). Referring back to
FIG. 5, two or more reference signal peaks may be identified in the
normalized MRS spectrum data 210 at step 404. Reference signal
peaks, as defined herein, are signal peaks corresponding to known
biomarkers. For example, FIG. 6 shows a non-limiting example of an
MRS spectrum on which 97 previously documented biomarker peaks
(Martinez-Bisbal et al, 2004) have been overlaid.
[0052] In one embodiment, the reference signal peaks representing
particular biomarkers may be identified by inspection of the raw
MRS spectrum data 101 by an expert practitioner. In another
embodiment, the reference signal peaks may be identified using an
automated identification method. The reference signal peaks may be
identified by the presence of a signal peak in close proximity to a
previously documented chemical shift of a known biomarker's signal
peak in another embodiment. In still another embodiment, the
reference signal peaks may be identified by the presence of the
peak at a relative position within a previously documented pattern
of signal peaks.
[0053] The chemical shift location of reference signal peaks in the
normalized MRS spectrum data 210 may be compared to the previously
documented standard chemical shifts at step 406, and the chemical
shifts of the reference signal peaks in the normalized MRS spectrum
data 210 may be moved to coincide with the standard chemical shifts
at step 408 to generate the recalibrated MRS spectrum data 212. In
addition, the location of all other chemical shifts in the MRS
spectrum may be optionally moved based on a linear interpolation
using the newly relocated reference signal peaks identified in step
404, in order to preserve the relative distance of the other
chemical shifts relative to the reference signal peaks at step
410.
[0054] As a non-limiting illustrative example, FIG. 7A and FIG. 7B
show an overlay of 13 MRS scans before and after recalibration,
respectively. In this example, the recalibration of the MRS scans
was performed based on two reference signal peaks identified by
inspection of the MRS scans by an expert practitioner.
Variance-Weighting Module
[0055] Referring to FIG. 8, a flow chart illustrates the operation
of a non-limiting exemplary embodiment of a variance-weighting
module 206 that may emphasize consistent signal peaks and depress
chemical noise in an MRS spectrum. At step 502, the
variance-weighting module 206 may access a recalibrated MRS
spectrum data 212 at step 504. The variance-weighting module 206
may divide each signal value at each chemical shift in the spectrum
by the weighting constant at the corresponding chemical shift from
a weighting spectrum to generate the weighted MRS spectrum data
214.
[0056] A weighting spectrum, as used herein, is a table of chemical
shifts and corresponding weighting constants. Alternatively, the
weighting spectrum may be defined as a summary of weighted spectrum
signals that includes a maximum weighted signal value. Weighting
constants, as defined herein, are numerical constants used to
increase or decrease the magnitude of the signal value at a given
chemical shift, based on a predetermined criterion, including but
not limited to the variation in signal values determined from a set
of reference MRS spectrum data at the given chemical shift.
[0057] In one non-limiting exemplary embodiment, the weighting
function is the average relative standard variation determined from
two or more sets of reference MRS scans, in which each set of
reference scans corresponds to a particular tissue type including
but not limited to normal or malignant. The two or more sets of
reference MRS scans include but are not limited to raw MRS spectrum
data 101, normalized MRS spectrum data 214, and recalibrated MRS
spectrum data 212. As a non-limiting illustrative example, FIG. 9
shows an exemplary weighting spectrum. In this example, the
relative standard deviations at each chemical shift were determined
for a set of reference recalibrated MRS spectrum data 212. The
relative standard deviation, as used herein, is an expression of
the variation in signal values relative to the average value of the
signal values for a plurality of MRS spectra data, as expressed by
formula (I):
Relative Standard Deviation=(Standard
Deviation)/(Average).times.100 (I)
[0058] In addition, in various embodiments, the relative standard
deviations may be determined for at least one other reference set
of MRS data including but not limited to raw MRS spectrum data 101,
normalized MRS spectrum data 214, and recalibrated MRS spectrum
data 212. In various embodiments, the relative standard deviations
of all reference sets may be averaged, yielding a weighting
function, including but not limited to the weighting function shown
in FIG. 9.
[0059] The relative standard deviation is a relatively large number
for a combination of high variation, as expressed by a large
standard deviation, or a relatively low overall value, as expressed
by a low average. Random noise, for example, typically has a large
variation around a zero value, resulting in a large relative
standard deviation. Because variance weighting is implemented by
dividing the signal value by the relative standard deviation, the
signal value of random noise may be reduced greatly relative to the
signal value of large-valued signals having relatively low
variation in the reference sets of raw MRS spectra data 101.
[0060] The scaling of the MRS spectrum data performed by the
variance-weighting module 206 represents a significant advancement
over existing techniques such as Fisher weighting, which applies a
single weighting factor consisting of the ratio of the variance
within a group to the ratio of variance between groups uniformly
across the signal values in the spectrum. By contrast, the
variance-weighting module 206 may derive weighting factors at each
individual chemical shift based on the relative standard deviation,
which represents the amount of variance relative to the average
value of the data. As a result, the signals most likely to contain
useful diagnostic information, i.e. those signals having a high
average value and low variance, are scaled up relative to random
noise.
Renormalization Module
[0061] FIG. 10 is a flow chart illustrating the operation of a
non-limiting exemplary embodiment of a renormalization module 208.
The renormalization module 208 at step 602 may process MRS spectrum
data including but not limited to scaled MRS spectrum data 214 and
may transform all signal values of the spectrum to range from a
normalized minimum value to a normalized maximum value. Because the
scaled MRS spectrum data 214 may have been normalized by an
embodiment of the normalization module 202, it may not be necessary
to subtract the minimum signal value from all other signal values,
since the minimum signal value may be the normalized minimum value.
Instead, the maximum signal value of the scaled MRS spectrum may be
determined at step 604, and all values of the MRS spectrum may be
divided by this value at step 606, yielding a final preprocessed
MRS spectrum data 103 that may be transferred to the output system
106 for subsequent analysis or storage in various embodiments.
System Components
[0062] MRS preprocessing system 100 may include particular
components for providing various functions as discussed above. In
particular, the computer readable media 104 may include volatile
media, nonvolatile media, removable media, non-removable media,
and/or other media or mediums that can be accessed by a general
purpose or special purpose computing device. For example, computer
readable media 104 may include computer storage media and
communication media, including computer readable media. Computer
storage media further may include volatile, nonvolatile, removable,
and/or non-removable media implemented in a method or technology
for storage of information, including but not limited to computer
readable instructions, data structures, program modules, and/or
other data. Communication media may, for example, embody computer
readable instructions, data structures, program modules,
algorithms, and/or other data, including as or in a modulated data
signal. The communication media may be embodied in a carrier wave
or other transport mechanism and may include an information
delivery method. The communication media may include wired and/or
wireless connections and technologies and may be used to transmit
and/or receive wired or wireless communications. Combinations
and/or sub-combinations of the systems, components, modules, and
methods and processes described herein may be made.
[0063] The input system 102 may include, for example, a hard disk
(not shown) that stores the raw MRS spectrum data 101 input files
that are read by the MRS preprocessing system 100. The input system
102 also may include a storage system that stores electronic data,
including but not limited to the raw MRS spectrum data 101 and
other electronic data files. The input system 102 also may be one
or more processing systems and/or communication systems that
transmit and/or receive electronic files and/or other electronic
document information or data through wireless and/or wire line
communication systems, and/or other data to the MRS preprocessing
system 100.
[0064] The output system 106 may include a communication system
that communicates data with another system or component. The output
system 106 may be a storage system that temporarily and/or
permanently stores data, including but not limited to input files,
intermediate data tables generated by the MRS preprocessing system
100, output files, and/or other data. The output system 106 also
may include a computer, one or more processors, one or more
processing systems, or one or more processes that further process
data. The output system 106 may otherwise include a monitor or
other display device, one or more processors, a computer, a
printer, another data output device, volatile and/or nonvolatile
memory, other output devices, computer readable media, a user
interface 108 for displaying data, and/or a combination of the
foregoing. The output system 106 may receive and/or transmit data
through a wireless and/or wire line communication system. The
output system 106 may be embodied by or in or operate using one or
more processors or processing systems, one or more distributed or
integrated systems, and/or computer readable media. The output
system 106 is optional for some embodiments.
Identification of Tissue Abnormalities Using Preprocessed MRS
Spectrum Data
[0065] Preprocessed MRS spectrum data 103 produced by various
embodiments of the MRS preprocessing system 100 may be used to
identify tissue abnormalities using a variety of methods. As one
non-limiting example, an expert practitioner may visually compare
the preprocessed MRS spectrum data 103 from the sample tissue to
one or more comparison MRS spectrum data from tissues having known
abnormalities to determine whether an abnormality is present in the
sample tissue. In another non-limiting example, an automated
pattern recognition algorithm may be used to automatically compare
the preprocessed MRS spectrum data 103 from the sample tissue to
one or more comparison MRS spectrum data from tissues having known
abnormalities to determine whether an abnormality is present in a
tissue.
[0066] Automated pattern recognition algorithms make use of known
statistical modeling techniques including but not limited to
principal component and discriminant analysis to match the signal
patterns of an unknown tissue to the signal patterns within a model
resulting from the analysis of the MRS signal patterns from tissues
having a variety of known abnormalities. A non-limiting example of
a software package that implements an automated pattern recognition
method is RESolve 1.2 (Hi-Res version, Colorado School of Mines).
The efficacy of any type of pattern recognition method may depend
upon at least several factors including the consistency of the data
analyzed.
[0067] The consistency of the MRS spectra data may depend upon at
least several factors including but not limited to variation in the
type of MRS instrument used to measure the MRS spectra data, the
variation in operating parameters within the same MRS instrument
over time, and the variation due to the practices of individual MRS
technicians when measuring the MRS spectra data. The various
embodiments of the MRS preprocessing system 100 may reduce much of
the variation between individual MRS spectra data by performing a
standardized set of processes to produce each preprocessed MRS
spectrum data 103, rendering the data in a more standardized form
for diagnostic purposes.
[0068] In one non-limiting exemplary embodiment, automated pattern
recognition methods may be used to compare the preprocessed MRS
spectrum data 103 from an unknown tissue type to a model derived
from a set of entire MRS spectra data from tissues having various
known abnormalities. In another embodiment, only a subset of the
signals from the preprocessed MRS spectrum data 103 from an unknown
tissue type corresponding to known biomarkers may be compared to a
model derived from subsets of the signals of MRS spectra
corresponding to the same biomarkers in tissues with known
abnormalities. In this embodiment, the standardization of the MRS
spectrum data made possible by the use of the MRS preprocessing
system 100 results in diagnostic efficacies that are comparable to
the diagnostic efficacies of pattern recognition methods that
compare entire MRS spectra. Because only a discrete subset of the
MRS spectra corresponding to the biomarkers are used in this
embodiment, this method of identifying an unknown tissue type may
be used for MRS spectra data obtained for a variety of spectra
resolutions. As a result, this method may be used in a wide variety
of laboratory and clinical settings equipped with MRS devices of
varying resolution. In addition, because this method is
resolution-independent, the standardized MRS spectra data from one
or more laboratory or clinical facilities may be shared and/or
combined as needed. A non-limiting example of a subset of the
signals from the preprocessed MRS spectrum data is provided in
Table 2 in the Examples below.
[0069] The preprocessed MRS spectrum data 103 may be derived from
any tissue sample capable of measurement by an MRS device without
limit. Non-limiting examples of tissue samples that may analyzed
using various embodiments include brain tissue, prostate tissue,
breast tissue, liver tissue, lung tissue, ovarian tissue,
testicular tissue, bladder tissue, tongue tissue, dermal tissue,
epidermal tissue, joint tissue, bone tissue, eye tissue, and kidney
tissue. In addition, non-limiting examples of tissue abnormalities
that may be identified using various embodiments include
malignancy, necrosis, neurotoxicity, delamination, hypertrophy,
hypotrophy, inflammation, and rheumatism.
[0070] In one illustrative non-limiting example, an embodiment of
the method for detecting a tissue abnormality may be used to
identify whether an unknown brain tissue sample possesses an
abnormality including but not limited to necrosis, metastasized
carcinoma, metastasized melanoma, demyelination, astrocytoma,
oligodendroglioma, meningioma, glioblastoma multiforme, or
ganglioglioma. In this example, an automated pattern recognition
method may be used to match the MRS spectrum pattern of the unknown
tissue sample to the model that relates the pattern of a MRS
spectrum to each of the possible tissue abnormalities, as well as
to the MRS spectrum pattern of normal brain tissue.
[0071] In another illustrative non-limiting example, an embodiment
of the method for detecting a tissue abnormality may be used to
determine the stage of a particular cancerous tissue. In this
example, an automated pattern recognition method may be used to
match the MRS spectrum pattern of the unknown cancerous tissue
sample to the model that relates the pattern of a MRS spectrum to
each of the possible cancer stages, as well as to the MRS spectrum
pattern of normal non-cancerous tissue.
[0072] Other non-limiting examples of applications of the MRS
preprocessing system 100 are provided in the Examples below.
Exemplary Embodiments
[0073] FIG. 11 is a block diagram showing a second non-limiting
exemplary embodiment of the MRS preprocessing system 100A that
preprocesses a normalized MRS spectrum data 210. In this
embodiment, the MRS preprocessing system 100A includes a chemical
shift recalibration module 204 that may recalibrate the normalized
MRS spectrum data 210, a variance-weighting module 206 that may
scale the recalibrated MRS spectrum data 212 at each chemical shift
up or down according to a predetermined weighting function to
generate a variance-weighted MRS spectrum data 214 and a
renormalization module 208 that may normalize the variance-weighted
MRS spectrum data 214 to produce a preprocessed MRS spectrum
103.
[0074] FIG. 12 is a block diagram showing a third non-limiting
exemplary embodiment of the MRS preprocessing system 100B that
preprocesses raw MRS spectrum data 101. In this embodiment, the MRS
preprocessing system 100B may include a normalization module 202 to
normalize the raw MRS spectrum data 101, a variance-weighting
module 208A that may scale the recalibrated MRS spectrum data 212
at each chemical shift of the normalized MRS spectrum data 210, and
a renormalization module 208 to normalize the variance-weighted MRS
spectrum data 214, resulting in a preprocessed MRS spectrum data
103. This particular embodiment does not include a chemical shift
calibration module 204, which simplifies the implementation of the
MRS preprocessing system 100A at the cost of somewhat lower
diagnostic efficacy, as presented in the examples below. This
embodiment may be suitable for an initial diagnostic procedure for
patients with unidentified abnormalities on traditional MR imaging.
Once a possible disease state is identified using this simplified
embodiment, the raw MRS spectrum data 101 could be preprocessed
using an embodiment that includes a chemical shift recalibration
module 204 and analyzed using a more accurate predictive diagnostic
model.
[0075] FIG. 13 is a block diagram showing a fourth non-limiting
exemplary embodiment of the MRS preprocessing system 100C that
preprocesses a normalized MRS spectrum data 210. In this
embodiment, the MRS preprocessing system 100B may include a
variance-weighting module 206A that processes the normalized MRS
spectrum data 210 and may scale the normalized MRS spectrum data
210 using the variance-based weighting function previously
discussed above. The resulting scaled MRS spectrum data 214 may be
normalized by the renormalization module 208 to generate a
preprocessed MRS spectrum data 103, which may be optionally
transferred to the output system 106 for storage or subsequent
analysis.
[0076] Other non-limiting exemplary embodiments may exclude the
normalization module 202 and/or the renormalization module 208. Yet
other non-limiting exemplary embodiments may perform the operations
of any combination of embodiments of the normalization module 202,
chemical shift recalibration module 204, variance-weighting module
206A, renormalization module 208 or any subset thereof in any
order.
Diagnostic Applications
[0077] The preprocessing of MRS spectra as described herein results
in MRS spectra of sufficient quality to develop diagnostic models
to identify a wide variety of different tissue types. Although the
chemical shift peaks corresponding to particular biomarkers used in
the preprocessing method may vary depending upon the particular
tissue to be diagnosed, the preprocessing methodology is generally
applicable. Non-limiting examples of diagnostic models that may be
developed using the preprocessed MRS spectra data are summarized in
Table 1:
TABLE-US-00001 TABLE 1 Application of Diagnostic Models Using
Pre-processed MRS Spectra Diagnostic Application Diagnostic Value
Identify glioblastoma multiforme Establish need for follow-up vs.
necrotic tissue therapy Discriminate astrocytoma tissue
Intervention vs. watchful waiting grades 2/3/4 Identify deep brain
(basal ganglia Improved diagnostic efficacy; and brainstem)
anomalies biopsy-based yield is non- diagnostic Diagnosis of
metastatic tumor Origin of primary tumor unknown Monitor brain
trauma tissue Need early correlation to eventual clinical outcome
(return to activity, symptom duration) WHO CNS Tumor Classification
Improved definition of prognosis System Lymphoma differential
diagnosis Define treatment options; (lymphoma vs. glial neoplasm)
non-surgical intervention is better for lymphomas Identify tumor
tissue Early diagnosis; define treatment options Identify tumor
tissue type - Define treatment options glioblastoma multiforme,
metastasis, or abscess Differential diagnosis of Define treatment
options oligodendroglioma tissue grades Differentiate early
Alzheimer's difficult at early stages; defines disease vs. mild
cognitive treatment options impairment vs. vascular dementia
Parkinson's disease vs. secondary Early diagnosis; defines
treatment Parkinson's disease vs. multiple options system atrophy
vs. corticobasil degeneration Huntingdon's Disease diagnosis Early
diagnosis; defines treatment options Human Prion Disease diagnosis
Early diagnosis; defines treatment (Creutzfeldt-Jakob disease,
options Gerstmann-Straussler- Scheinker syndrome, fatal familial
insomnia, variant Creutzfeldt-Jakob disease, iatrogenic
Creutzfeldt-Jakob disease, kuru) Amyotrophic lateral sclerosis
Early diagnosis and staging; diagnosis defines treatment options
Repeated Brain Injury predict recovery or long term cognitive
defect Breast Cancer vs. benign tissues Determine temporal window
for re- injury; predict responder vs. non- responder; predict
metastasis to lymph nodes Diagnosis of other cancers (ovarian,
Early diagnosis; defines treatment prostate, bone, lung,
pancreatic, options colon) Diagnose arthritis and other joint Early
diagnosis; defines treatment diseases options Diagnose any disease
state using Early diagnosis; defines treatment measured changes in
composition or options concentrations of biological components
within any tissue; gout, arterial plaque, nephritis, high blood
sugar, and many others
EXAMPLES
[0078] The following examples illustrate various aspects of the
embodiments.
Example 1
Qualitative Effects of Chemical Shift Recalibration on MRS
Spectra
[0079] To assess the qualitative effects of various preprocessing
techniques on the quality of MRS spectra data from various tissue
types, the following experiment was conducted.
[0080] Multiple MRS brain scans associated with nine common brain
disease categories and representative normal brain tissue were
obtained with institutional approval (UAMS Human Research Advisory
Committee, University of Arkansas Medical Sciences Assurance
M-1451, IORG0000345). The number of MRS scans obtained in each
sample class are summarized in Table 2:
TABLE-US-00002 TABLE 2 MRS Scans Used in Preprocessing Assessment
Studies Scans Used for Used for Model External Sample Class
Training Validation Total Normal (N) 20 41 61 Necrosis (X) 2 0 2
Metastasized Carcinoma (C) 6 0 6 Metastasized Melanoma (Z) 2 0 2
Demyelination (D) 3 0 3 Astrocvtoma (A) 14 0 14 Oligodendroglioma
(0) 20 4 24 Meningioma (M) 3 0 3 Glioblastoma multiforme (G) 20 2
22 Ganglioglioma (Z) 2 0 2
[0081] All MRS spectra data were collected at the University of
Arkansas for Medical Sciences (UAMS) using three GE Signa 1.5 Tesla
MRIs (Echospeed clinical model LX, GE Medical Systems, Waukesha,
Minn., USA) and GE's double spin echo sequence (PROBE-P). Data were
acquired using repetition times of either 1400,1500, or 2000 ms and
echo times (TEs) of 35 or 144 ms. All scans were acquired as an
extension of the clinical imaging services performed for these
patients or from healthy volunteers.
[0082] With the exception of contralateral brain scans, the
identities of training set scans were confirmed by biopsy.
Contralateral scans were assumed to be normal based on the absence
of any MRI-detectable anomaly.
[0083] The TE 35 ms data included signals from molecules with
fairly short T2 times, including lipids, myo-inositol (MI),
glutamine, glutamate, and other amino acids from N-acetyl aspartate
(NAA), creatine (Cr), and choline (Cho). In addition, because the
TE 35 ms data are also susceptible to water suppression artifacts,
these scans may also have contained signals from macromolecules. By
contrast, the TE 144 ms data contained only signals from NAA, Cr,
Cho, and in some cases lactate (Lac).
[0084] All data were processed using GE SA/GE software with
zero-filling to 4096 points, 2 Hz exponential line broadening, and
automatic phasing using the water peak (Webb et al., 1994).
Uncorrected MRS spectra data were saved as ASCII files in (ppm,
signal intensity) pairs using a proprietary modification to the
SA/GE software. A pathological diagnosis was appended to each MRS
spectrum file. Spectral samples were blind-coded to protect patient
identity and sent to the National Center for Toxicological Research
for analysis.
[0085] For the chemical shift re-calibration, 97 potential chemical
shift locations associated with biomarkers from high-grade glioma
biopsies (Martinez-Bisbal et al, 2004) were considered. The 97
potential chemical shift locations are shown superimposed on a
typical MRS scan of astrocytoma tissue in FIG. 6, and the
individual biomarkers considered for this experiment are summarized
in Table 3:
TABLE-US-00003 TABLE 3 Chemical Shifts of Potential Brain Anomaly
Biomarkers Chemical Shift (ppm) Biomarker 0.90 Fatty acids 0.94
Isoleucine 0.95 Leucine 0.96 Leucine 0.98 Valine 1.00 Isoleucine
1.04 Valine 1.25 Isoleucine 1.09 Fatty acids 1.30 Fatty acids 1.33
Lactate, Threonine 1.36 Fatty acids 1.46 Isoleucine, Lysine 1.47
Alanine 1.59 Fatty acids 1.67 Arginine 1.69 Lysine 1.71 Leucine
1.72 Arginine 1.90 .gamma.-Aminobutunic acid, Lysine 1.91 Acetate
1.97 Isoleucine 2.00 Proline 2.03 N-acetyl aspartate 2.04 Glutamate
2.03 Fatty acids 2.06 Proline 2.11 Glutamate 2.12 Methionine 214
Glutamine 2.19 Methionine 2.25 Fatty acids 2.27
.gamma.-aminobutyric acid 2.28 Valine 2.34 Glutamate 2.36 Proline
2.39 Succinate, Malate 2.44 Glutamine 2.49 N-acetyl aspartate 2.63
Methionine 2.65 Aspartic acid 2.68 N-acetyl aspartate, Malate 2.80
Aspartic acid 2.82 Fatty Acids 2.86 Asparagine 2.96 Asparagine 3.01
.gamma.-Aminobuturic acid, Lysine 3.03 Creatine 3.05 Lysine 3.06
Tyrosine 3.12 Ethanolamine, Phenylalanine 319 Cholina, Tyrosina
3.20 Phosphocholine 3.22 Arginine 3.23 Glycerophosphocholine 3.24
MI 3.26 Taurine 3.30 Phenylalanine 3.34 Proline 3.40
.alpha.-Glucose 3.42 .beta.-Glucose, Taurine, Proline 3.46
.beta.-Glucose 3.49 .beta.-Glucose 3.52 Choline 3.53 mI 3.55
Glycine 3.56 Glycerol 3.57 Phosphocholine 3.58 Threonine 3.61
Valine, ml 3.64 Glycerol 3.67 Glycerophosphocholine, isoleucine
3.69 .alpha.-Glucose 3.72 .beta.-Glucose 3.74 Leucine 3.75
Glutamate 3.76 Glutamine 3.77 Lysine, Alanine 3.78 .alpha.-Glucose
3.79 Ethanolamine, Glycerol 3.83 .alpha.-Glucose, Serine 3.85
Methionine 3.87 Arginine 3.89 Aspartic Acid 3.90 .beta.-Glucose
3.93 Creatine, Tyrosine 3.94 Serine 3.97 Serine 3.99 Asparagine
4.00 Phenylalanine 4.05 MI 4.06 Choline 4.11 Lactate 4.12 Proline
4.18 Phosphocholine 4.25 Threonine 4.28 Glycerophosphocholine
[0086] For re-calibration, two manually identified reference proton
MRS peaks were chosen based on the criteria that the MRS peaks were
strongly expressed and far apart from one another within a
spectrum. Appropriate references biomarker peaks varied on a
case-by-case basis. Besides proximity to an expected location, the
identity of a useful reference peak was sometimes inferred from its
part in a pattern of adjacent peaks from unrelated molecules. This
selection strategy was used successfully within groups of MRS
spectra from the same lesion class. Identification of reference
peaks was also inferred in some cases by the location and intensity
of strong peaks resulting from other protons in the same biomarker
molecule.
[0087] Using the chosen reference peaks and custom-designed
recalibration/binning software to facilitate operations, each MRS
spectrum was translated and re-calibrated. The software moved the
selected reference peaks from their observed chemical shifts to
their respective reference locations. The software also linearly
interpolated all other data points in the MRS spectrum to new
positions to generate the re-calibrated spectrum data.
[0088] A consistent trend in the reference peak assignments
relative to the location of the corresponding peak in a spectrum
was observed. At both ends of the MRS spectrum the necessary peak
location shift was in the same direction and almost the same
distance but the re-calibration shift required in the low ppm
domain was slightly less than the shift at the high ppm end. The
gross similarity in the magnitude of the required shift supported
the hypothesis that the need for re-calibration arose from a
phenomenon including but not limited to magnetic field strength
variation as a function of tumor depth, a phenomenon that had a
parallel effect on all protons within a voxel. The difference in
relative correction scale with chemical shift highlighted the
necessity of at least a two-point recalibration with interpolation
to recalibrate the MRS spectrum.
[0089] The effectiveness of re-calibration is depicted
qualitatively in FIGS. 7A and 7B, which shows a selected region of
raw astrocytoma scans prior to and after recalibration
respectively.
Example 2
Qualitative Effects of Variance Weighting on MRS Spectra
[0090] To assess the qualitative effects of variance weighting
techniques on the quality of MRS spectra from various tissue types,
the following experiment was conducted.
[0091] The MRS scan data sets described in Example 1 were
preprocessed using a weighting scheme based on spectra consistency.
The spectra intensities from the MRS scans were weighted to reduce
random variation (noise) relative to the signal peaks from
biomarkers that consistently appeared in the scans. One goal of the
weighting was to increase the peak resolution and available
information content of MRS without biasing tissue classification
results.
[0092] To determine the weighting function, recalibrated spectra
determined using the methods of Example 1 were used to ensure that
biomarker peaks would occur at consistent locations on the chemical
shift axis of each spectrum. In each tissue class, the relative
standard deviation (RSD) was calculated for each chemical shift
data point using the signal values from all spectra within each
tissue class. This process yielded an RSD variance spectrum across
the chemical shift domain for each class.
[0093] The average of all RSD variance spectra from all classes at
each chemical shift was then determined, resulting in a single
spectrum of weighting factors. This weighting function, when
divided into an MRS spectrum, would not bias the determination of
tissue class identity using pattern recognition techniques, but
would discriminate against spectrum regions associated with
chemical noise relative to those in which a peak might appear.
[0094] FIG. 9 shows a summary of the overall average Relative
Standard Deviations (RSDs) as a function of proton chemical shift.
These values were used as divisors to scale the MRS spectra at each
respective chemical shift. The largest average RSDs appear as peaks
in FIG. 9, representing chemical shift locations that had
relatively high variance in signal values, but were not
consistently associated with MRS peaks or proton NMR biomarkers of
brain tissue. When used as a sequence of divisors in a weighting
process, relatively high average RSD values in the weighting
spectrum attenuate the signal values and relative low RSD values
inflate the original MRS intensities at their respective chemical
shift locations.
[0095] Variance weighting substantially increased the resolution of
peaks in the MRS scans and produced distinguishable peaks in scans
where previously the peaks had been barely observable. Further,
variance weighting eliminated any remaining chemical shift
variation and positioned most biomarker peaks at their expected
locations. Provisionally identified biomarker peaks
(Martinez-Bisbal el al, 2004) that appeared exactly at previously
reported positions in this experiment included acetate (1.91 ppm);
proline (2.00 ppm); glutamate (2.04 ppm); glutamine (2.14 ppm);
valine (2.28 ppm); succinate and malate (2.39); N-acetyl aspartate
(2.49 ppm); choline (3.19 ppm); ethanolamine, phenylalanine (3.12
ppm); taurine (3.26 ppm); .beta.-glucose, taurine, and proline
(3.42 ppm); .beta.-glucose (3.46 ppm); and glycerol (3.46 ppm).
Further, the weighting process preserved the relative peak
intensity pattern associated with astrocytomas even though the
weighting function was based on RSDs averaged for spectra of all
available tissue types.
Example 3
Effects of MRS Spectrum Preprocessing on the Efficacy of Automated
Diagnostic Methods
[0096] To assess the effects of MRS spectrum preprocessing on the
efficacy of automated pattern recognition methods used to identify
different tissue types based on patterns of biomarker peaks, the
following experiment was conducted.
[0097] MRS scans obtained using the methods described in Example 1
were preprocessed and analyzed using automated pattern recognition
techniques. Three preprocessing strategies were investigated in
this experiment: 1) chemical shift re-calibration, 2) baseline
correction, and 3) variance-weighting. Only the TE 35 ms scans were
used for preprocessing evaluations.
[0098] The effectiveness of the preprocessing strategies on the
diagnostic efficacy of the MRS spectra was assessed by comparing
diagnostic results calculated using RESolve 1.2 (Hi-Res version)
obtained from the Colorado School of Mines. A subset of the 92 MRS
scans described in Table 2 of Example 1 was used for modeling and
cross-validation experiments. The RESolve software provides several
alternative types of pattern recognition; in this experiment,
Principal Component and Discriminant Analysis was used to develop
quantitative results. The 47 MRS scans omitted from the original
MRS scan data set were used subsequently as an external validation
set for comparing the two methods determined to yield the highest
diagnostic efficacy.
[0099] FIG. 14 summarizes the results of a Principal Components and
Discriminant Analysis performed by the RESolve 1.2 using minimally
preprocessed MRS spectra with the spectra being normalized but
otherwise unmodified prior to analysis. FIG. 15 summarizes a
similar analysis performed using pre-processed MRS spectra in which
the spectra were subjected to normalization, recalibration, and
variance-weighting prior to analysis. A comparison of FIG. 14 with
FIG. 15, shows that the preprocessing of the spectra categories
reduced the separation of the data within the cluster for each
particular tissue type and increased the separation distance
between the clusters from different tissue types, thereby
indicating that the diagnostic model derived using the preprocessed
spectra discriminates more distinctly between the different tissue
types.
[0100] Table 4 summarizes the results of the assessment of the
diagnostic efficacy of MRS spectra that were preprocessed using
various combinations of preprocessing strategies. Table 4 lists the
cumulative predictive accuracy for classification among nine
categories of tissue types in the four 92-scan data sets under left
one out (LOO) cross-validation using all 380 data points of each
MRS spectrum. Variability-weighting dramatically improved the
diagnostic accuracy of the MRS spectra data relative to MRS spectra
that were preprocessed using only normalization or only
recalibration. A combination of normalization, recalibration, and
variance-weighting resulted in the highest diagnostic efficacy of
any of the preprocessing strategies assessed.
TABLE-US-00004 TABLE 4 Diagnostic Efficacy of Pattern Recognition
Methods Using Pre-Processed MRS Spectra Cumulative Predictive
Preprocessing Strategy Accuracy Normalization only 31.5%
Normalization + Recalibration only 30.4% Normalization + Variance
Weighting + 89.1% Renormalization Normalization + Recalibration +
94.6% Variance Weighting + Renormalization
Example 4
Sensitivity of the Efficacy of Automated Diagnostic Methods to the
Number of Spectrum Data Points in the Diagnostic Model
[0101] To assess the sensitivity of the efficacy of a diagnostic
model to the number of MRS spectra data points incorporated into
the development of the model, the following experiment was
conducted.
[0102] Two diagnostic models were constructed using the 92 MRS
spectra data points that were preprocessed using normalization,
recalibration, and variance-weighting, in which the nine tissue
types were classified using left one out (LOO) cross-validation as
described in Example 3. The first diagnostic model incorporated all
380 data points within each spectrum, as described in Example 3.
The second diagnostic model incorporated a subset of 97 data points
within each spectrum corresponding to the 97 biomarkers listed in
Table 2 of Example 1 above.
[0103] An external validation of the diagnostic models was
performed using the 47 MRS spectra reserved for the validation. The
47 spectra are listed in Table 2 of Example 1 above. All MRS
spectra were subjected to preprocessing that included
normalization, recalibration, and variance-weighting prior to
analysis using the diagnostic models.
[0104] FIG. 16 summarizes the results of a Principal Components
analysis performed using all MRS spectra, with the 47 validation
spectra shown as unknown types. As shown in FIG. 16, the 41
validation spectra obtained from normal tissue appeared to cluster
tightly over the normal tissue data obtained previously. The
remaining six spectra (four obtained from oligodendroglioma tissue
and two obtained from glioblastoma multiforme tissue) were situated
among the abnormal tissue data obtained previously.
[0105] The two data models were used to diagnose the original 92
MRS scans used to develop the models as well as the 47 validation
spectra into different tissue categories. The tissue categories
predicted by the diagnostic models were compared to the known
classification of each spectrum to assess the accuracy of each
model. The accuracy of the predicted tissue type of the two models
is summarized in Table 5. The use of the diagnostic model developed
using the subset of biomarker data points from the model training
set resulted in a predictive accuracy of 100% for the spectra
within the external validation data set, compared to 95.7% accuracy
obtained using all data from the spectra.
TABLE-US-00005 TABLE 5 Comparison of Diagnostic Efficacy of Pattern
Recognition Models Using Pre-Processed MRS Spectra Cumulative
Predictive Accuracy Biomarker MRS Spectra Full Spectrum Spectrum
Subset Analyzed (380 data points) (97 data points) Model Training
Set 94.6% 93.5% (n = 92) External Validation Set 95.7% 100% (n =
47)
Example 5
Development of Tissue Category Subclasses Using Automated
Diagnostic Methods
[0106] To assess development of an automated diagnostic method that
categorized subclasses of tissue types, the following experiment
was conducted.
[0107] A subset of the scans used for model training summarized in
Table 2 of Example 1 were used to develop a diagnostic model using
methods similar to those described in Example 3. In this
experiment, only the fourteen astrocytoma scans and the 20
glioblastoma multiforme scans were used to develop the diagnostic
model. The astrocytoma scans were divided into two categories
corresponding to grade 2 or grade 3. The glioblastoma multiforme
scans were assigned to a category corresponding to grade 4;
glioblastoma multiforme is an abnormal astrocytoma tissue type also
known as grade 4 astrocytoma. The diagnostic model developed using
this method has an R.sup.2 of 0.82.
[0108] The results of this experiment indicated that the method of
developing diagnostic models using preprocessed MRS spectra may be
useful in discriminating between different grades within the same
tissue category.
[0109] It should be understood from the foregoing that, while
particular embodiments have been illustrated and described, various
modifications can be made thereto without departing from the spirit
and scope of the invention as will be apparent to those skilled in
the art. Such changes and modifications are within the scope and
teachings of this invention as defined in the claims appended
hereto.
References
[0110] 1. Martinez-Hisbal, M. Carmen; Marti-Bonmati, Luis; Piquer,
Jose; Revert, Antonio; Ferrer, Pilar; Llacer, Josi L.; Piotio,
Martial; Assemat, Olivier; and Celda, Bernardo; (2004) "IH and I3C
HR-MAS spectroscopy of intact biopsy samples ex vivo and in vivo 1H
MRS study of human high grade gliomas", NMR Biomed. 2004;
17:191-205. [0111] 2. Webb P. B., Sailasuta N., Kohler S. J., Raidy
T., Moats R. A., Hurd R. E. (1994) "Automated single-voxel proton
MRS: technical development and multisite verification." Magn.
Reson. Med. 31:365-373.
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