U.S. patent application number 10/102444 was filed with the patent office on 2003-01-09 for quantitation and standardization of magnetic resonance measurements.
This patent application is currently assigned to Board of Regents of the University of Washington. Invention is credited to Smith, Justin P..
Application Number | 20030006770 10/102444 |
Document ID | / |
Family ID | 22603353 |
Filed Date | 2003-01-09 |
United States Patent
Application |
20030006770 |
Kind Code |
A1 |
Smith, Justin P. |
January 9, 2003 |
Quantitation and standardization of magnetic resonance
measurements
Abstract
An MRI apparatus and method useful for both industrial
applications and medical applications is provided. The apparatus
and procedures are capable of estimating the value of a continuous
property, such as concentration, viscosity or the like by
interpolating or extrapolating from a model derived from training
sets of data representing measurements of samples with known
properties. A number of techniques are provided for objectifying
the analysis. Cluster analysis techniques can be used to
supplement, assist or replace subjective judgments by trained
operators. Calculations or judgments regarding similarity can be
made with respect to stored libraries of signatures, particularly
where the library of stored signatures is obtained objectively,
e.g., using cluster analysis, standardization and calibration. The
signatures can be expanded signatures which include non-MR as well
as MR data. Inhomogeneities in the field of a particular MR device
can be corrected for based on measurements of a calibration
standard having a homogeneous make up. MR measurements taken
through different planes of a body or different times can be
standardized by including, in at least some of the fields of view,
a calibration standard which has a known MR signature.
Inventors: |
Smith, Justin P.; (Kirkland,
WA) |
Correspondence
Address: |
TOWNSEND AND TOWNSEND AND CREW, LLP
TWO EMBARCADERO CENTER
EIGHTH FLOOR
SAN FRANCISCO
CA
94111-3834
US
|
Assignee: |
Board of Regents of the University
of Washington
Seattle
WA
98105
|
Family ID: |
22603353 |
Appl. No.: |
10/102444 |
Filed: |
March 19, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10102444 |
Mar 19, 2002 |
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08166449 |
Dec 13, 1993 |
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5413477 |
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08166449 |
Dec 13, 1993 |
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08814304 |
Mar 10, 1997 |
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5818231 |
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08814304 |
Mar 10, 1997 |
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08153118 |
Nov 15, 1993 |
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5644232 |
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08153118 |
Nov 15, 1993 |
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07883565 |
May 15, 1992 |
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5311131 |
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Current U.S.
Class: |
324/309 ;
324/300; 324/314; 324/318 |
Current CPC
Class: |
F23C 2900/09002
20130101; F23C 7/06 20130101; F23L 2900/15043 20130101; G01R 33/58
20130101; F23C 6/045 20130101; F23C 2202/10 20130101; Y02E 20/348
20130101; G01R 33/56 20130101; F23C 2900/06041 20130101; F23C 9/00
20130101; F23C 2201/20 20130101 |
Class at
Publication: |
324/309 ;
324/300; 324/314; 324/318 |
International
Class: |
G01V 003/00 |
Claims
What is claimed:
1. A computer-implemented method for estimating the magnitude of at
least a first continuous property of a first sample of a first
substance based on nuclear magnetic resonance measurements
comprising: obtaining a first plurality of magnetic resonance
responses comprising a set of measurements of each of a plurality
of calibration samples of a material which includes said first
substance, said plurality of calibration samples being different
from said first sample and including a first plurality of values of
said first continuous property, each set of said plurality of
nuclear magnetic resonance measurements including at least first
and second different pulse sequences; defining a first mapping from
a first set of values, based on said first plurality of nuclear
magnetic resonance measurements, to a set of values of said first
continuous property, said mapping being continuous over at least a
first domain of values of said first continuous property; obtaining
a second plurality of nuclear magnetic resonance measurements of
said first sample using at least said first and second pulse
sequences; and calculating an estimated value of said first
property by applying said first mapping to a second set of values
based on said second plurality of nuclear magnetic resonance
measurements wherein said estimated value is different from any of
said first plurality of values of said first continuous
property.
2. A computer-implemented method, as claimed in claim 1, wherein
said substance comprises non-living material.
3. A computer-implemented method, as claimed in claim 1, wherein
said substance comprises a living body.
4. A computer-implemented method, as claimed in claim 1, wherein
said substance comprises in vitro material derived from a living
body.
5. A computer-implemented method, as claimed in claim 1, wherein
said body is a living human body.
6. A computer-implemented method, as claimed in claim 1, wherein
said step of defining said first mapping includes defining a
mapping which predicts a value of said first property based on a
plurality of MR measurements.
7. A computer-implemented method, as claimed in claim 1, said first
mapping provides a value of said first property using a method
selected from among PLS, PCR, LWR, PPR, ACG, MARS AND NN.
8. A computer-implemented method, as claimed in claim 1, wherein
said step of obtaining a first set of MR measurements includes
forming a plurality of congruent sets of images.
9. A computer-implemented method, as claimed in claim 1, further
comprising standardizing said first plurality of MR measurements,
using at least a first reference object.
10. A computer implemented method, as claimed in claim 1, further
comprising standardizing said second plurality of MR measurements,
using at least a first calibration object.
11. A computer-implemented method, as claimed in claim 1, further
comprising storing, in memory, values characterizing said
mapping.
12. A computer-implemented method, as claimed in claim 1, further
comprising: defining a second mapping from a set of values, based
on said-first plurality of MR measurement, to a set of values of a
second property different from said first property and calculating
an estimated value of said second property using said second
mapping.
13. A computer-implemented method, as claimed in claim 1, wherein
said first plurality of values consists of only two values of said
first property.
14. A computer-implemented method, as claimed in claim 1, wherein
said step of defining a mapping includes defining groups of said MR
measurements using cluster analysis.
15. A computer-implemented method for estimating the magnitude of
at least a first continuous property of a sample of a first
substance of unknown composition based on nuclear magnetic
resonance measurements, comprising: obtaining a first plurality of
measurements using a MR apparatus, said measurements comprising a
set of measurements of each of a plurality of calibration samples
of a material which includes said first substance, said plurality
of calibration samples being different from said first sample and
including a first plurality of values of said first property, each
set of said plurality of nuclear magnetic resonance measurements
including a first group of measurements which said MR apparatus has
a first configuration and a second group of measurements while said
MR apparatus has a second configuration, wherein said first and
second groups of measurements are independent; defining a first
mapping from a first set of values, based on said first plurality
of measurements, to a set of values of said first continuous
property; obtaining a second plurality of measurements, using an MR
apparatus, of said first sample, including at least measurements
with said first configuration and measurements with said second
configuration; and calculating an estimated value of said first
property using said first mapping.
16. A method for identifying which, among a first plurality of
regions in a first non-homogenous part of a body are most similar
to a second plurality of regions in a second non-homogeneous part
of a body, comprising: obtaining MR measurements of each of said
first and second parts of said body; defining first and second
pluralities of clusters of regions of said first and second part of
said body, respectively, using cluster analysis, the regions in
each cluster of regions of said first and second pluralities of
clusters having similar MR measurements; forming first visual
displays, each of said first visual displays, including said first
part of said body, said first visual displays including visual
indicia identifying at least some of said first plurality of
clusters or regions; selecting one of said first plurality of
clusters, based on said first visual displays, as a first cluster
of interest; forming second visual displays, each of said second
visual displays including said second part of said body, said
second visual displays including visual indicia identifying at
least some of said second plurality of clusters; selecting one of
said second plurality of clusters, based on said second visual
displays, as a second cluster of interest; and calculating a
measure of similarity between the MR measurements for said first
clusters of interest and the MR measurements for said second
cluster of interest.
17. A method for identifying which, among a first plurality of
regions in a first non-homogenous part of a body are most similar
to a second plurality of regions in a second non-homogeneous part
of a body, comprising: obtaining MR measurements of each of said
first and second parts of said body; defining first and second
pluralities of clusters of regions of said first and second part of
said body, respectively, using cluster analysis, the regions in
each cluster of regions of said first and second pluralities of
clusters having similar MR measurements; calculating a measure of
similarity between MR measurements for a plurality of pairs of
clusters, each pair of clusters in said plurality of pairs of
clusters comprising a cluster from said first plurality of clusters
and a cluster from said second plurality of clusters; selecting at
least some of said pairs of clusters, based on said measures of
similarity, as pairs of interest; and forming visual displays
including visual indicia distinguishably identifying at least said
pairs of interest.
18. A method of using magnetic resonance imaging (MRI) to produce
an image of a body, the method comprising the steps of: using an
MRI apparatus to produce a training set comprising one or more
training samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image;
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image; producing similarity data, based on cluster
analysis, indicating, for each test sample, the degree of
similarity between the test sample and the training samples; and
producing a display based upon the similarity data.
19. A method for identifying the composition of regions of a body
comprising: storing a first plurality of MR measurements of a
substance having a first composition; storing a second plurality of
MR measurements of a substance having a second composition;
obtaining MR measurements of a non-homogeneous portion of said
body; identifying at least a first region of said non-homogeneous
portion of said body by applying cluster analysis to said MR
measurements; calculating measures of similarity between the MR
measurements for said first region and at least said first and
second plurality of MR measurements; and identifying one of said
first composition and said second composition as the composition of
said region based on said measures of similarity.
20. A method, as claimed in claim 18, further comprising displaying
at least one image of said body, with visual indicia based on
composition of said region.
21. A method, as claimed in claim 19, further comprising displaying
a plurality of images of said body in real time to provide an
indicate of changes or movement of said first or second
composition.
22. A method for identifying the composition of regions of a body
comprising: obtaining MR measurements of a portion of said body,
including said region; obtaining a second measurement of said
portion of said body, said second measurement being different from
said MR measurement; and identifying the composition of said region
using both said MR measurement and said second measurement.
23. A method, as claimed in claim 21, wherein said step of
obtaining MR measurements includes recalling at least some of a
library of stored MR measurements from a memory device.
24. A method, as claimed in claim 21, wherein said step of
identifying includes calculating a measurement of similarity by
combining said first measurement of similarity with said second
measurement of similarity.
25. A method, as claimed in claim 21, wherein said portion of said
body is a substantially non-homogeneous portion.
26. A method for estimating the volume occupied by a substance
within a body comprising: obtaining first MR measurements of a
first plurality of regions in said body, said first plurality of
regions substantially defining a first surface passing through a
portion of said body each of said first plurality of regions being
substantially representative of a volume of said body having a
dimension substantially transverse to said first surface; obtaining
a second MR measurement of a second plurality of regions in said
body, said second plurality of regions substantially defining a
second surface, different from said first surface, each of said
second plurality of regions being substantially representative of a
volume of said body having a dimension substantially transverse to
said second surface; identifying a plurality of target regions in
said body based on said MR measurements, said target regions having
MR measurements which indicate said target region comprising said
substance, said target region including at least some of said first
and second pluralities of regions; and calculating the sum of the
volumes which said target region are representative of.
27. A method of using magnetic resonance imaging (MRI) to produce
an image of a body, the method comprising the steps of: using an
MRI apparatus to produce a training set comprising one or more
training samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image;
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image; producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples; and calculating a volume by identifying those
pixels having at least a first degree of similarity and multiplying
the number of said pixels by the volumes of said body represented
by said pixels.
28. A method of accounting for inhomogeneities in fields produced
by an MR apparatus comprising: positioning a first substantially
homogeneous reference material at least within a first field of
view of said MR apparatus; obtaining first MR measurements in a
first plurality of region of said calibration material, said first
plurality of regions being within said first field of view;
calculating correction factors for at least some of said first
plurality of regions, based on said MR measurements of said
calibration material; obtaining second MR measurements in a second
plurality of regions of a test substance, said second plurality of
regions being within said first field of view; and combining said
correction factor with said second MR measurements to provide
corrected MR measurements for said second plurality of regions.
29. A method, as claimed in claim 27, wherein said step of
calculating comprises dividing first values by the MR intensity for
each of said first plurality of regions.
30. A method, as claimed in claim 27, wherein said first value can
be an average intensity.
31. A method, as claimed in claim 27, wherein each of said second
plurality of regions can be spatially coupled with one of said
first plurality of regions and the step of combining can include
multiplying the intensity for each of said second plurality of
regions by the corresponding correction factor.
32. A method, as claimed in claim 27, wherein said reference
material comprises water.
33. A method, as claimed in claim 27, wherein said first MR
measurements comprise measurements using at least first and second
sequences and further comprising calculating separate correction
factors for said first and second sequences.
34. A method of using magnetic resonance imaging (MRI) to produce
an image of a test object, the method comprising the steps of:
using an MRI apparatus to produce a training set comprising one or
more training samples, the training set being formed from a
plurality of congruent first images of a training region of a first
object, each first image being produced using an MRI pulse sequence
different from the pulse sequences used to produce the other first
images, each first image comprising an array of pixels, each
training sample comprising a spatially aligned set of pixels from
each first image, at least some of said first images including an
image of at least one training set reference object; using an MRI
apparatus to produce a test set comprising a plurality of test
samples, the test set being formed from a plurality of congruent
second images of a test region of the test object, the second
images being produced using the same MRI pulse sequences as the
first images, each second image comprising an array of pixels, each
test sample comprising a spatially aligned set of pixels from each
second image, at least some of said second images including an
image of at least one test set reference object substantially
similar to said test set reference object; correcting at least part
of said test set based on differences between said image of said
training set reference object and said image of said test set
reference object; producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples; and producing a display based upon the
similarity data.
35. A method of using magnetic resonance imaging (MRI) to produce
an image of a test object, the method comprising the steps of:
using an MRI apparatus to produce a training set comprising one or
more training samples, the training set being formed from a
plurality of congruent first images of a training region of a first
object, each first image being produced using an MRI pulse sequence
different from the pulse sequences used to produce the other first
images, each first image comprising an array of pixels, each
training sample comprising a spatially aligned set of pixels from
each first image, at least some of said first images including an
image of at least one training set reference object; using an MRI
apparatus to produce a test set comprising a plurality of test
samples, the test set being formed from a plurality of congruent
second images of a test region of the test object, the second
images being produced using the same MRI pulse sequences as the
first images, each second image comprising an array of pixels, each
test sample comprising a spatially aligned set of pixels from each
second image, at least some of said second images including an
image of at least one test set reference object substantially
similar to said test set reference object; producing similarity
data indicating, for each test sample, the degree of similarity
between the test sample and the training samples; correcting at
least part of said similarity data based on differences between
said image of said training set reference object and said image of
said test set reference object; and producing a display based upon
the similarity data.
36. A method of using magnetic resonance imaging (MRI) to produce
an image of a test object, the method comprising the steps of:
using an MRI apparatus to produce a training set comprising one or
more training samples, the training set being formed from a
plurality of congruent first images of a training region of a first
object, each first image being produced using an MRI pulse sequence
different from the pulse sequences used to produce the other first
images, each first image comprising an array of pixels, wherein
each pixel comprises a pixel value corresponding to the intensity
of a magnetic resonance signal from a corresponding position within
the object, each training sample comprising a spatially aligned set
of pixels from each first image and wherein the training set
includes at least one spatial correlation image corresponding to
and congruent with one of the first images, the spatial correlation
image comprising an array of spatial correlation pixels, each
spatial correlation pixel having a pixel value that is determined
on the basis of a textural feature, wherein each training sample
comprises a spatially aligned set of pixels from each first image
and from each first spatial correlation image; using an MRI
apparatus to produce a test set comprising a plurality of test
samples, the test set being formed from a plurality of congruent
second images of a test region of the test object, the second
images being produced using the same MRI pulse sequences as the
first images, each second image comprising an array of pixels,
wherein each pixel comprises a pixel value corresponding to the
intensity of a magnetic resonance signal from a corresponding
position within the object, each test sample comprising a spatially
aligned set of pixels from each second image wherein the test set
includes at least one second spatial correlation image
corresponding to and congruent with one of the second images, the
second spatial correlation image comprising an array of second
spatial correlation pixels, each second spatial correlation pixel
having a pixel value that is determined on the basis of a textural
feature, each test sample comprising a spatially aligned set of
pixels from each second image and from each second spatial
correlation image; producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples; and producing a display based upon the
similarity data.
37. A method of using magnetic resonance imaging (MRI) to produce
an image of a body, the method comprising the steps of: using an
MRI apparatus to produce a training set comprising one or more
training samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image;
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image; producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples; producing a first image based on at least
some of said second congruent images producing a second image which
displays only those portions of the first image which are within a
user-defined similarity threshold of a portion of said training
set; and subtracting said second image from said first image to
produce a third image.
38. A method of using magnetic resonance imaging (MRI) to produce
an image of a body, the method comprising the steps of: using an
MRI apparatus to produce a training set comprising one or more
training samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image;
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image; producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples; producing a first image based on at least
some of said second congruent images calculating, for each of a
plurality of pixels within at least a part of said first image, a
difference value indicating the magnitude of the difference between
the MR data corresponding to said pixel and at least a first
portion of the MR data from said training set; and producing a
second image including visual indicia indicating, for said
plurality of pixels at least first and second different levels
based on said difference value.
39. A method of using magnetic resonance imaging (MRI) to produce
an image of a test object, the method comprising the steps of:
using an MRI apparatus to produce a training set comprising one or
more training samples, the training set being formed from a
plurality of congruent first images of a training region of a first
object, each first image being produced using an MRI pulse sequence
different from the pulse sequences used to produce the other first
images, each first image comprising an array of pixels, each
training sample comprising a spatially aligned set of pixels from
each first image; using an MRI apparatus to produce a test set
comprising a plurality of test samples, the test set being formed
from a plurality of congruent second images of a test region of the
test object, the second images being produced using the same MRI
pulse sequences as the first images, each second image comprising
an array of pixels, each test sample comprising a spatially aligned
set of pixels from each second image; producing similarity data
representing distance in a multi-dimensional measurement space; and
producing a display based upon the similarity data.
Description
[0001] This application is a continuation-in-part application of
commonly-assigned U.S. Ser. No. 07/883,565, filed May 15, 1992
incorporated herein by reference
[0002] The present invention relates to magnetic resonance imaging
(MRI), and in particular to quantitation and standardization of
both medical and industrial applications of MRI.
BACKGROUND OF THE INVENTION
[0003] Magnetic resonance measurements have been used in both
non-medical and medical applications. In a typical non-medical
application, a sample or a body of non-living matter is subjected
to a static magnetic field and an oscillating radiofrequency field.
The radiofrequency field electrically excites hydrogen atoms in the
sample or body. After the oscillating field is turned off, the
intensity of proton oscillation is measured, e.g., using an
antenna, typically configured to detect the intensity of
oscillations in a plurality of locations in a two-dimensional
plane.
[0004] In a typical medical application of MRI, a patient is placed
within the bore of a large, donut-shaped magnet. The magnet creates
a static magnetic field that extends along the long (head-to-toe)
axis of the patient's body. An antenna (e.g., a coil of wire) is
also positioned within the bore of the large magnet, and is used to
create an oscillating radiofrequency field that selectively excites
hydrogen atoms (protons) in the patient's body into oscillation.
The oscillating field is then turned off, and the antenna is used
as a receiving element, to detect the proton oscillations as a
function of position within the body. Typically, the intensity of
the oscillations is measured throughout a two-dimensional plane.
When the intensities are displayed as a function of position in
this plane, the result is an image that often bears a striking
resemblance to the actual anatomic features in that plane. Although
MRI typically involves visual display of data, "imaging" can
involve purely digital analysis and output so that, in this
context, "imaging" does not necessarily require
visually-perceptible output.
[0005] The intensity of proton oscillations detected at a given
point in the patient's body is proportional to the proton density
at that point. Because different types of tissues have different
proton densities, different tissue types usually have different
image intensities, and therefore appear as distinct structures in
the MR image. However, the signal intensity also depends on
physical and chemical properties of the tissues being imaged. In a
simplified model of MRI, the detected signal intensity, as a
function of position coordinates x and y in the plane being imaged
is proportional to:
(1-e.sup.-TR/T.sup..sub.1)e.sup.-TE/T.sup..sub.2 (1)
[0006] The parameters TR (recovery time) and TE (echo delay time)
are under the control of the operator of the MR imaging system, and
are constants for any given image. However, T.sub.1 and T.sub.2 are
functions of the tissue under examination, and therefore vary with
position in the x-y plane. By suitable selection of parameters TR
and TE, either the T.sub.1 or the T.sub.2 term in Equation 1 can be
made to dominate, thereby producing so-called "T.sub.1-weighted"
and "T.sub.2-weighted" images, respectively.
[0007] One of the more important medical uses to which MRI has been
put to date is to noninvasively scan a portion of a patient's body,
in an attempt to identify benign or malignant tumors. When MRI is
used in this fashion, it is necessary to have some methodology for
concluding that a given portion of an MR image represents tumor, as
opposed to other tissue types such as fat, cyst, etc. One known
approach to identifying tissue type has been to acquire multiple MR
images of the same region of the patient's body, using different
imaging parameters, e.g., using different value of the TR and TE
parameters. To take a simplified example, if it were known that a
given tumor produced a high image intensity at a first parameter
setting, a low image intensity at a second parameter setting, and a
high image intensity at a third parameter setting, then a portion
of a patient's body that produced that pattern of intensities
(high, low, high) could be tentatively identified as tumor.
[0008] Pattern recognition approaches of this type are described in
U.S. Pat. No. 5,003,979. This patent describes a system for the
detection and display of lesions in breast tissue, using MRI
techniques.
[0009] Many previous applications of magnetic resonance
measurements have been directed to determining whether a substance
or tissue is or is not of a particular type (e.g., whether a
portion of a body being imaged is or is not fat). Other
applications have been directed to determining whether a portion of
a body being imaged falls into one of a small number of discrete
categories (e.g., fat, cyst, or tumor). Non-parametric magnetic
resonance imaging techniques have typically not been used for
effectively and efficiently quantitizing a continuous property
(such as viscosity or concentration of a substance within a body or
sample).
[0010] In previous MRI techniques, analysis of results have often
included a subjective or non-automatic component, such as a step of
classifying or identifying portions of an image using judgment of a
skilled observer. Accordingly, it would be useful to include
techniques to more objectively or automatically categorize or
analyze MR data.
[0011] In many cases, comparison of the pattern of intensities of a
patient's tissue to "standard" patterns for different tissue types
does not produce results of sufficient accuracy. One problem
appears to be that attempts to define a single "standard" pattern
for a given tissue type does not take sufficient account of
possible variability in tissue of a given type. Another problem
appears to be that there is substantial variability from one
patient or sample to the next as well as from one MRI machine to
the next or within different regions or fields of view of the same
MRI machine.
[0012] Cancer treatment often includes detecting when a primary
tumor has spread to other sites in the patient's body, to produce
so-called secondary tumors, known as metastases. This process,
using MRI or other imaging techniques, is often complicated by the
fact that a remote lesion discovered during staging could represent
either a metastasis or a benign incidental finding. A number of
benign lesions (such as hepatic hemangiomas and nonfunctioning
adrenal adenomas) occur as frequently in patients with a known
primary tumor as they do in the general population.
[0013] Resolving this dilemma requires additional imaging studies
or biopsy, but often significant uncertainty persists. Biopsy may
expose the patient to substantial risk when the lesion in the brain
or mediastinum, or when the patient has impaired hemostasis. Even
when biopsy does not present a significant risk to the patient, it
may be technically challenging, such as sampling focal lesions in
vertebral marrow.
SUMMARY OF THE INVENTION
[0014] For the reasons set forth above, it would be useful to have
a method that could use MR data to estimate the value of a
continuous property such as concentration, viscosity or the like
for both industrial applications and medical applications. It would
further be useful to have a method which reduces or eliminates the
need for subjective analysis of MRI data.
[0015] It would also be useful to provide a method that takes
account of variability among "standard" substances or tissue types,
takes account of variability from patient to patient, among
different MRI machines and within different regions or fields of
view of the same MRI machine. Particularly with regard to medical
applications, it would be useful to provide a method that could
noninvasively measure the similarity between a known primary tumor
and a remote lesion of unknown tissue type. The clinician would use
the measured similarity to determine the likelihood that the two
lesions represent the same tissue. Such a method could be used to
distinguish a pathological fracture from a benign osteoporotic
compression fracture in a patient with a known primary tumor.
Similarly, the method could be used to distinguish a metastasis
from an infarction in a patient with lung cancer who presents with
a supratentorial solitary enhancing lesion. Using the computed
similarity to determine the likelihood that two lesions represent
the same tissue would significantly improve the confidence of
noninvasive imaging diagnosis.
[0016] According to the present invention, magnetic resonance data
is provided in a way which is useful for both medical and
non-medical (e.g., industrial) applications. In one embodiment, an
MRI apparatus is used to produce a training set comprising one or
more training samples. The training set is formed from a congruent
set of first images of a training region of the body being studied.
The term "congruent" refers to the fact that each of the first
images represents the same physical slice or plane through the
body. In an industrial application, the training region may be,
e.g., a sample of a known substance or a sample of a substance with
a particular characteristic such as, e.g., a sample of oil having a
known viscosity. In a medical application, the training region may
be, e.g., the region of a known primary tumor. The first images are
produced using a predetermined set of MRI pulse sequences that
differ from one another. Each first image comprises an array of
pixels, and each training sample comprises a spatially aligned set
of pixels from each first image.
[0017] MRI apparatus is also used to produce a test set comprising
a plurality of test samples. The test set is formed from a
congruent set of second images of a test region of the test body.
In an industrial application, the test region may comprise, e.g,.
an unknown substance or a substance with unknown characteristics,
e.g., an oil with unknown viscosity. In a medical application, the
test region may comprise, e.g., a region to be scanned for a
secondary tumor. The second images are produced using the same MRI
pulse sequences as the first images. Each second image comprises an
array of pixels and each test sample comprises a spatially aligned
set of pixels from each second image.
[0018] According to one embodiment, for each test sample, one then
produces similarity data indicating the degree of similarity
between the test sample and the training samples. A display is then
produced based upon the similarity data. The display identifies the
test samples having the highest degree of similarity to the
training samples. For example, one of the second images may be
displayed using a conventional gray scale, while the most similar
pixels are highlighted in color. In an industrial application, the
display might, e.g., highlight those samples, among a plurality of
oil samples, having a viscosity matching the viscosity of a
training set. In the secondary tumor example, the regions of the
second image that are highlighted in color will correspond to those
regions most similar to the first region (the training set) which
comprises the primary tumor. The color highlighted regions will
therefore identify possible sites of secondary tumors.
[0019] In another aspect, the invention also provides for the
generation of spatial correlation images based on each of the first
and second images, and the use of the spatial correlation images in
combination with the first and second images to produce the
training and test samples. Instrument standardization techniques
may also be applied, to minimize errors when the first and second
images are acquired from different planes through the body, or at
different times. In another aspect, the present invention may
provide a technique for suppressing or enhancing certain tissue
types in an MR image.
[0020] According to one aspect of the invention an MRI apparatus
and method useful for both industrial applications and medical
applications is provided. The apparatus and procedures are capable
of estimating the value of a continuous property, such as
concentration, viscosity or the like by interpolating or
extrapolating from a model derived from training sets of data
representing measurements of samples with known properties. A
number of techniques are provided for objectifying the analysis.
Cluster analysis techniques can be used to supplement, assist or
replace subjective judgments by trained operators. Calculations or
judgments regarding similarity can be made with respect to stored
libraries of signatures, particularly where the library of stored
signatures is obtained objectively, e.g., using cluster analysis,
standardization and calibration. The signatures can be expanded
signatures which include non-MR as well as MR data. Inhomogeneities
in the field of a particular MR device can be corrected for based
on measurements of a reference standard having a homogeneous make
up. MR measurements taken through different planes of a body or
different times can be standardized by including, in at least some
of the fields of view, a reference standard which has a known MR
signature.
[0021] According to one embodiment, the invention includes a method
for identifying which, among a first plurality of regions in a
first non-homogenous part of a body are most similar to a second
plurality of regions in a second non-homogeneous part of a body,
comprising obtaining MR measurements of each of said first and
second parts of said body, defining first and second pluralities of
clusters of regions of said first and second part of said body,
respectively, using cluster analysis, the regions in each cluster
of regions of said first and second pluralities of clusters having
similar MR measurements, forming first visual displays, each of
said first visual displays, including said first part of said body,
said first visual displays including visual indicia identifying at
least some of said first plurality of clusters or regions,
selecting one of said first plurality of clusters, based on said
first visual displays, as a first cluster of interest, forming
second visual displays, each of said second visual displays
including said second part of said body, said second visual
displays including visual indicia identifying at least some of said
second plurality of clusters selecting one of said second plurality
of clusters, based on said second visual displays, as a second
cluster of interest, and calculating a measure of similarity
between the MR measurements for said first clusters of interest and
the MR measurements for said second cluster of interest.
[0022] According to another embodiment, the invention includes a
method for identifying which, among a first plurality of regions in
a first non-homogenous part of a body are most similar to a second
plurality of regions in a second non-homogeneous part of a body,
comprising obtaining MR measurements of each of said first and
second parts of said body, defining first and second pluralities of
clusters of regions of said first and second part of said body,
respectively, using cluster analysis, the regions in each cluster
of regions of said first and second pluralities of clusters having
similar MR measurements, calculating a measure of similarity
between MR measurements for a plurality of pairs of clusters, each
pair of clusters in said plurality of pairs of clusters comprising
a cluster from said first plurality of clusters and a cluster from
said second plurality of clusters, selecting at least some of said
pairs of clusters, based on said measures of similarity, as pairs
of interest, and forming visual displays including visual indicia
distinguishably identifying at least said pairs-of interest.
[0023] According to another embodiment, the invention includes a
method of using magnetic resonance imaging (MRI) to produce an
image of a body, the method comprising the steps of using an MRI
apparatus to produce a training set comprising one or more training
samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image,
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image, producing similarity data, based on cluster
analysis, indicating, for each test sample, the degree of
similarity between the test sample and the training samples, and
producing a display based upon the similarity data.
[0024] According to another embodiment, the invention includes a
method for identifying the composition of regions of a body
comprising storing a first plurality of MR measurements of a
substance having a first composition, storing a second plurality of
MR measurements of a substance having a second composition,
obtaining MR measurements of a non-homogeneous portion of said
body, identifying at least a first region of said non-homogeneous
portion of said body by applying cluster analysis to said MR
measurements, calculating measures of similarity between the MR
measurements for said first region and at least said first and
second plurality of MR measurements, and identifying one of said
first composition and said second composition as the composition of
said region based on said measures of similarity. In this case, the
method can also include displaying at least one image of said body,
with visual indicia based on composition of said region and/or
displaying a plurality of images of said body in real time, to
provide an indication of changes or movement of said first or
second composition and/or a step of standardization.
[0025] According to another embodiment, the invention includes a
method for identifying the composition of regions of a body
comprising obtaining MR measurements of a portion of said body,
including said region, obtaining a second measurement of said
portion of said body, said second measurement being different from
said MR measurement, and identifying the composition of said region
using both said MR measurement and said second measurement. The
step of obtaining MR measurements can include recalling at least
some of a library of stored MR measurements from a memory device.
The step of identifying include calculating a measurement of
similarity by combining said first measurement of similarity with
said second measurement of similarity. Preferably, the portion of
said body is a substantially non-homogeneous portion.
[0026] According to another embodiment, the invention can include a
method for estimating the volume occupied by a substance within a
body comprising obtaining first MR measurements of a first
plurality of regions in said body, said first plurality of regions
substantially defining a first surface passing through a portion of
said body each of said first plurality of regions being
substantially representative of a volume of said body having a
dimension substantially transverse to said first surface, obtaining
a second MR measurement of a second plurality of regions in said
body, said second plurality of regions substantially defining a
second surface, different from said first surface, each of said
second plurality of regions being substantially representative of a
volume of said body having a dimension substantially transverse to
said second surface, identifying a plurality of target regions in
said body based on said MR measurements, said target regions having
MR measurements which indicate said target region comprising said
substance, said target region including at least some of said first
and second pluralities of regions, and calculating the sum of the
volumes which said target region are representative of.
[0027] According to another embodiment, the invention can include a
method of using magnetic resonance imaging (MRI) to produce an
image of a body, the method comprising the steps of using an MRI
apparatus to produce a training set comprising one or more training
samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image,
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image, producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples, and calculating a volume by identifying those
pixels having at least a first degree of similarity and multiplying
the number of said pixels by the volumes of said body represented
by said pixels.
[0028] According to another embodiment, the invention includes a
method of accounting for inhomogeneities in fields produced by an
MR apparatus comprising positioning a first substantially
homogeneous reference material at least within a first field of
view of said MR apparatus, obtaining first MR measurements in a
first plurality of region of said reference material, said first
plurality of regions being within said first field of view,
calculating correction factors for at least some of said first
plurality of regions, based on said MR measurements of said
reference material, obtaining second MR measurements in a second
plurality of regions of a test substance, said second plurality of
regions being within said first field of view, and combining said
correction factor with said second MR measurements to provide
corrected MR measurements for said second plurality of regions. The
steps of calculating comprise dividing first values by the MR
intensity for each of said first plurality of regions. The first
value can be an average intensity. Each of said second plurality of
regions can be spatially coupled with one of said first plurality
of regions and the step of combining can include multiplying the
intensity for each of said second plurality of regions by the
corresponding correction factor. The reference material can be,
e.g., a cuprous sulfate solution. The first MR measurements can
comprise measurements using at least first and second sequences and
the method can also include calculating separate correction factors
for said first and second sequences and/or different locations
within the test substance.
[0029] According to another embodiment, the invention includes a
method of using magnetic resonance imaging (MRI) to produce an
image of a test object, the method comprising the steps of using an
MRI apparatus to produce a training set comprising one or more
training samples, the training set being formed from a plurality of
congruent first images of a training region of a first object, each
first image being produced using an MRI pulse sequence different
from the pulse sequences used to produce the other first images,
each first image comprising an array of pixels, each training
sample comprising a spatially aligned set of pixels from each first
image, at least some of said first images including an image of at
least one training set reference object, using an MRI apparatus to
produce a test set comprising a plurality of test samples, the test
set being formed from a plurality of congruent second images of a
test region of the test object, the second images being produced
using the same MRI pulse sequences as the first images, each second
image comprising an array of pixels, each test sample comprising a
spatially aligned set of pixels from each second image, at least
some of said second images including an image of at least one test
set reference object substantially similar to said test set
reference object, producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples, correcting at least part of said similarity
data based on differences between said image of said training set
reference object and said image of said test set reference object,
and producing a display based upon the similarity data.
[0030] According to another embodiment, the invention can include a
method of using magnetic resonance imaging (MRI) to produce an
image of a body, the method comprising the steps of using an MRI
apparatus to produce a training set comprising one or more training
samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image,
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image, producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples, producing a first image based on at least
some of said second congruent images, producing a second image
which displays only those portions of the first image which are
within a user-defined similarity threshold of a portion of said
training set, and subtracting said second image from said first
image to produce a third image.
[0031] According to another embodiment, the invention can include a
method of using magnetic resonance imaging (MRI) to produce an
image of a body, the method comprising the steps of using an MRI
apparatus to produce a training set comprising one or more training
samples, the training set being formed from a plurality of
congruent first images of a training region of the body, each first
image being produced using an MRI pulse sequence different from the
pulse sequences used to produce the other first images, each first
image comprising an array of pixels, each training sample
comprising a spatially aligned set of pixels from each first image,
using an MRI apparatus to produce a test set comprising a plurality
of test samples, the test set being formed from a plurality of
congruent second images of a test region of the same body, the
second images being produced using the same MRI pulse sequences as
the first images, each second image comprising an array of pixels,
each test sample comprising a spatially aligned set of pixels from
each second image, producing similarity data indicating, for each
test sample, the degree of similarity between the test sample and
the training samples, producing a first image based on at least
some of said second congruent images, calculating,-for each of a
plurality of pixels within at least a part of said first image, a
difference value indicating the magnitude of the difference between
the MR data corresponding to said pixel and at least a first
portion of the MR data from said training set, and producing a
second image including visual indicia indicating, for said
plurality of pixels at least first and second different levels
based on said difference value.
[0032] According to another embodiment, the invention can include a
method of using magnetic resonance imaging (MRI) to produce an
image of a test object, the method comprising the steps of using an
MRI apparatus to produce a training set comprising one or more
training samples, the training set being formed from a plurality of
congruent first images of a training region of a first object, each
first image being produced using an MRI pulse sequence different
from the pulse sequences used to produce the other first images,
each first image comprising an array of pixels, each training
sample comprising a spatially aligned set of pixels from each first
image, using an MRI apparatus to produce a test set comprising a
plurality of test samples, the test set being formed from a
plurality of congruent second images of a test region of the test
object, the second images being produced using the same MRI pulse
sequences as the first images, each second image comprising an
array of pixels, each test sample comprising a spatially aligned
set of pixels from each second image, producing similarity data
representing distance in a multi-dimensional measurement space
producing a display based upon the similarity data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] FIG. 1 is a schematic perspective view of an MRI imaging
apparatus.
[0034] FIG. 2 illustrates the concept of a set of congruent
images.
[0035] FIGS. 3A-3C illustrate three techniques for forming the
training and test sets.
[0036] FIG. 4 is a flow chart showing the principal steps of one
embodiment of the invention.
[0037] FIG. 5 illustrates the concept of first and second nearest
neighbor pixels.
[0038] FIG. 6 illustrates the combination of spatial correlation
images with the original images to form the training or test
set.
[0039] FIG. 7 illustrates adjustment of a portion of an MR image
containing a predetermined tissue type.
[0040] FIG. 8 is a graph showing the conversion of similarity data
into an image.
[0041] FIGS. 9A and 9B are MR images illustrating fat suppression
according to an embodiment of the present invention.
[0042] FIG. 10 is a schematic and block diagram of an MR process
using cluster analysis;
[0043] FIG. 11 is a schematic and block diagram similar to FIG. 10,
but showing comparison prior to display;
[0044] FIG. 12 is a schematic and block diagram of an MR process
which estimates a value of a continuous property;
[0045] FIG. 13 is a schematic and block diagram of an MR process
similar to that of FIG. 12, but using higher-order methods;
[0046] FIG. 13 is a block flow diagram of a software-implemented
process according to one embodiment;
[0047] FIGS. 15A-15F depict the results of an application of an
embodiment of the invention to analysis of household liquids where
cross-hatching indicates regions identified as similar to a
training set;
[0048] FIGS. 16A-16B depict the results of an application of an
embodiment of the invention to analysis of a collection of apples;
and,
[0049] FIGS. 17A-17B depict the results of an application of an
embodiment of the invention to analysis of a variety of wines where
two types of cross-hatching indicate regions identified as similar
to two different training sets; and
[0050] FIG. 18 is a graph showing development of a calibration made
for a continuous property.
DESCRIPTION OF THE PREFERRED EMBODIMENT
[0051] FIG. 1 presents a simplified schematic view of a
conventional apparatus for performing magnetic resonance imagining.
The apparatus comprises housing 12, computer 14 that serves as an
operator console, power supply module 16, and signal processing
module 18. Housing 12 has the form of a hollow cylinder that
surrounds the object on which imaging is to be performed. In the
depicted embodiment, imaging is being performed on a human patient
20. The present invention can, however, be applied to a wide
variety of types or collections of test bodies for various
industrial applications. For example, the object(s) to be imaged
can include a crate of apples (e.g., to identify apple varieties,
condition, size, etc.), a case of wine bottles (to identify type,
variety, vintage quality, condition, etc.), a block of frozen fish
(to identify species, freshness, etc.) or a chromatography column
(to identify and/or track progress of a substance).
[0052] The housing includes field coil 22 that is used to create a
static magnetic field along the central cylindrical axis (z axis)
of the housing. The housing also includes antenna 24 that is used
both to apply an oscillating radiofrequency field, and then to
detect the response function, i.e., radiofrequency signals produced
by the body in response to the applied static and oscillating
fields. The signals detected by the antenna are coupled to signal
processing module 18 where they are amplified, conditioned, and
digitized for storage in computer 14. The computer processes the
stored data and produces and displays an image of one or more
planes or slices 26 through the body.
[0053] Unlike computed tomography (CT), magnetic resonance (MR)
imaging generates data that are well-suited for quantitative
analysis. This is because the MR signal intensity is determined by
several variables; hence MR data are said to be multidimensional.
It is the multidimensional nature of MR signals that allows them to
be analyzed by the group of multivariate statistics known as
pattern recognition methods.
[0054] Pattern recognition methods have become widely used in
science and medicine because they can achieve greater accuracy with
lower cost than can traditional methods of data analysis. For
example, suppose that we wish to identify an unknown chemical
compound by comparison to a library of standard compounds. One
approach is to obtain a proton nuclear magnetic resonance (NMR)
spectrum of the compound and to compare it to the spectra of known
standards. By using an NMR spectrometer of sufficiently high
resolution, even closely-related compounds can often be
distinguished from one another. However, the accuracy of even these
instruments is limited, and their limited availability make this
approach infeasible for many investigators.
[0055] An alternative approach is to use pattern recognition
methods. Instead of trying to identify a compound by making a
single high-resolution measurement, the pattern recognition
approach relies on combinations of low-resolution measurements. For
example, spectra of the unknown compounds would be obtained from
low resolution MR, near-infrared, and mass spectrometers.
Multivariate statistics would then be used to compare these three
spectra to a library of reference spectra. Combining low resolution
measurements made by different modalities usually results in more
accurate identification than could be achieved by a high-resolution
MR spectrometer alone.
[0056] The ability of pattern recognition methods to recognize
similarities between samples is related to the discriminating
variance of the data that describe the samples. The greater the
discriminating variance of the data, the greater the potential
resolution-of the pattern recognition method. It is often possible
to obtain greater discriminating variance by combining several
low-resolution measurements made on different modalities that can
be obtained with measurements made on a single high-resolution
instrument.
[0057] With conventional MR imaging, the user prospectively chooses
pulse sequences that are believed most likely to answer the
question being studied. With the present invention, however, the
user applies sequences that have been chosen to maximize the
information (variance) acquired from a the sample being tested. The
user then applies pattern recognition techniques to the data to
retrospectively answer the specific clinical question.
[0058] The application of pattern recognition techniques to MRI is
based on the acquisition of multiple images taken of the same
region of a body. The views differ from one another, however,
because they are each acquired using different MRI pulse sequences,
i.e., using different parameter settings on the MRI apparatus. A
set of images acquired in this way are said to be congruent to one
another.
[0059] FIG. 2 schematically illustrates a set of eight congruent
images 31-38. All images are acquired from the same slice of plane
through a body, using different parameter settings for each image.
In one embodiment, images 31-38 are all acquired using the same MR
instrument, as close in time to one another as practical. In other
embodiments, the images may be acquired using different MR
instruments and/or may be acquired at different times. Each image
comprises a rectangular or square array of pixels, represented by
pixel 41 of image 31. By way of example, there may be 256 pixels
along one direction (the frequency encoding dimension), and 64-256
pixels along the other direction (the phase encoding dimension),
depending upon the particular pulse sequence used. However, other
numbers of pixels could also be used. Images 32-38 include pixels
42-48, respectively, that correspond to pixel 41, in that they
represent measurements made at the same physical position within
the body.
[0060] It is important to recognize that the acquired resolution of
the array (256.times.64 for example) usually differs from the
displayed resolution of the array (typically 512.times.512). The
acquired array is usually interpolated to 512.times.512, and the
interpolated array is then mildly smoothed (typically using a
low-pass filter). Both of these operations are performed by the
magnetic resonance imager to improve the subjective appearance of
the images. The pixel based operations of the present invention may
be performed either on the acquired pixels or on the pixels that
have been interpolated and smoothed for display. In general, the
latter option will be more convenient, and is therefore
preferred.
[0061] A collection of pixels from the same relative positions
within a set of congruent images, and therefore from the same
physical position within a body, are referred to herein as a
"sample". There is one such sample associated with each pixel
position in the region covered by images 31-38. Sample 50 can be
thought of as a very low resolution spectrum that contains
information concerning the nature of the patient's tissue at the
corresponding pixel position. Sample 50 can also be thought of as a
vector in a measurement space having eight dimensions.
[0062] As previously described, it is desirable for the data
represented by a congruent set of images to have as much
discriminating variance as possible. This means that the particular
parameter settings used to generate the images need to be selected
with care, to maximize the usefulness of the data. For the purpose
of discriminating tumor from other tissue types, it has been found
that the images are preferably generated using the following
standard MR pulse sequences: a T.sub.1-weighted spin-echo sequence
(one image); a six-echo multiple spin-echo (ME-6) sequence (six
images); and a short inversion time inversion recovery (STIR)
sequence (one image). Suitable echo times for ME-6 sequence are
26/52/78/104/130/156 ms, with TR of 1500 ms. For the STIR sequence,
suitable parameters are TR 1800-2000 ms, and an inversion time of
110 ms. This particular combination of pulse sequences generates an
eight-image data set having a large variance, and is well-suited to
the requirements for multivariate analysis.
[0063] In the context of an industrial application, images may be
generated using, as an example, the ME-6 sequence, one or more STIR
sequences and a T.sub.1-weighted spin-echo sequence. In some
industrial applications, it may be useful to use additional STIR
sequences and/or field-echo or other sequences.
[0064] Many other pulse sequences and combinations of pulse
sequences can be used for practicing the present invention. One
example of another pulse sequence is T.sub.1-weighted gradient echo
sequence, a fast T.sub.2-weighted spin or gradient echo sequence,
and a spin or gradient echo sequence adapted for fat suppression.
Fat suppression sequences are described in Tien, Robert D., "Fat
Suppression MR Imaging in Neuroradiology: Techniques and Clinical
Application," American Journal of Roentgenology 158:369-379,
February 1992, herein incorporated by reference. Magnetization
transfer sequences and diffusion sequences may be suitable for
certain applications. Contrast materials can also be used to
produce a contrast enhanced T.sub.1-weighted image. In addition,
other spin-echo sequences can be used, with different multiples.
For example, 4-echo multiple spin sequence will produce excellent
results in many cases. On some MRI devices, an ME-4 sequence has
the advantage that it can automatically acquire multiple stacked
slices, in a manner typical of most T.sub.1-weighted and STIR
sequences. For all sequences used, any parameters available with
the sequence can, of course, be adjusted to maximize the usefulness
of the invention for particular applications. For example, the
inversion time for a STIR sequence can generally be adjusted in the
range of 30-200MS, with the higher inversion times generally being
suitable for higher field strength systems. With gradient echo
sequences, the RF flip angle can be adjusted to maximize the
discriminating variance of the data.
[0065] Two general approaches for characterizing samples can be
used. In one approach, samples can be characterized based upon
their similarity to prior, known patterns for a particular
substance or tissue. In one example, the prior, known MR patterns
represent a composite or average from a number of known samples.
This approach, however, does not take into account the variability
that may be present in the substance or tissue which forms the
prototype. Accordingly, another embodiment involves collecting and
storing a library of MR response functions (signatures), spectra or
both patterns or spectra for a plurality of samples of a particular
substance or tissue.
[0066] A second approach involves comparing a sample or region of a
body being studied to the spectra for another region or sample from
the same body. In the context of a medical application, this
approach could involve a comparison of samples from one portion of
a patient to other samples for the same patient. For example,
referring to FIG. 3A, a congruent set 60 of images is first
obtained for a patient. A first group of one or more samples is
then selected as training set 62, while a second group of samples
is then selected as test set 64. Training set 62 may lie within a
known primary tumor, while test set 64 may be an area to be scanned
for the presence of a secondary tumor related to the primary
tumor.
[0067] Once training set 62 and test set 64 have been selected, one
then determines the degree of similarity, or the "distance",
between each sample in test set 64 and the training set. Suitable
techniques for providing a similarity measurement are discussed
below. In one approach, the distance from the test sample to each
training sample is determined, and then the minimum of these
distances is selected. In another approach, an average training
sample is computed, and the distance from the test sample to the
average training sample is determined.
[0068] Once a distance or similarity measure has been determined
for each test set sample, one of the images making up test set 64
is displayed, with the "most similar" pixels (e.g., the one percent
most similar pixels) highlighted. A preferred highlighting
technique is to display the most similar pixels in color,
superimposed on a conventional gray scale display of one of the
images of the test set. In a medical application, the resulting
display has proved to be clinically valuable for permitting a
practitioner to identify the extent, if any, to which a primary
tumor represented by the training set has spread to regions
encompassed by the test set. In an industrial application, the
technique will be used, e.g., to verify that all the fish in a
large block of frozen fish are the same species and/or are in the
same condition of freshness.
[0069] FIGS. 3B and 3C illustrate different techniques for
selecting the training and test sets. In FIG. 3B, one obtains two
sets 66, 68 of congruent images, for example from two different
slices or planes through a body. A training set 70 is selected from
set 66, while the entire second set 68 is used as the test set.
This variation permits the similarity measurement technique of the
present invention to be used to measure the similarity of any two
sites within the body, not just two sites within the same image
plane.
[0070] FIG. 3C illustrates the case in which a first set 72 is
acquired at one point in time, and a portion of set 72 is used to
form training set 76. At a later point in time, which could be
days, weeks or months later, a second congruent set 74 is obtained
through the same region of the patient's body, and used to form the
test set. In a medical application, this variation can be used
e.g., to trace the development of a single tumor and assess its
response to therapy, as well as to track the spread of the tumor to
other sites in the patient's body. In an industrial application,
this variation can be used, e.g., to track the movement of a
substance through a chromatography column.
[0071] It will be understood that the approaches illustrated in
FIGS. 3A-3C are not exhaustive, and that other variations could
also be used. For example, the techniques of FIGS. 3B and 3C could
be combined, to track the spread of a tumor both in time, and to
remote sites in a patient's body.
[0072] FIG. 4 provides a flow chart illustrating steps that can be.
used to carry out any of the procedures illustrated in FIGS. 3A-3C,
to track the spread of a primary tumor. In step 80, a conventional
MR imaging apparatus is used to obtain a first set of multiple
congruent images of a region of the patient's body that is believed
to contain a primary tumor. In step 82, each of the images in the
first set is preferably subjected to a spatial correlation
procedure which is one class of techniques aimed at the use of
texture characteristics of a data set. Two such techniques are
outlined in FIGS. 5 and 6. These examples of texture features
include angular second moment, contrast, correlation, sum of
squares (variance), inverse difference moment, sum average, sum
variance, sum entropy, entropy, difference variance, difference
entropy, information measures of correlation, and maximal
correlation coefficient, as described in Robert M. Haralick, et
al., "Textural Features for Image Classification," IEEE
Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, No. 6,
November, 1973, incorporated herein by reference.
[0073] Referring to FIG. 5, P represents any pixel in any of images
in the first set. For pixel P, the eight bordering pixels, labelled
1 in FIG. 5, are referred to as the first nearest neighbor pixels,
while the next group of 16 pixels, labelled 2, are referred to as
the second nearest neighbor pixels. In spatial correlation step 82
shown in FIG. 4, each of the "original" images in the first set is
processed, separately from the other images, to generate two new
images. In the first new image, each pixel has a value equal to the
average value of the first nearest neighbor pixels. In the second
new image, each pixel has a value equal to the average of the
second nearest neighbor pixels. This process is performed for each
of the original images in the first set. If there were eight
original first set images (as illustrated, for example, FIG. 2),
then this step will produce a total of 24 images as shown in FIG.
6. Stack 110 represents the 8 original first set of images; stack
112 represents the 8 new images generated by first nearest neighbor
averaging, while stack 114 represents the eight new images produced
by the second nearest neighbor averaging. Thus, as a result of the
spatial correlation step, there are now a total of 24 congruent
images representing a single slice through the body. Thus each
sample for this slice has a total of 24 intensity values associated
with it.
[0074] Returning to FIG. 4, the next step 84 is to select the
training set, i.e., a subset of samples in this slice that contain
the primary tumor under investigation. Various approaches can be
used for selection of the training set. In one approach, selection
of the training set involves the exercise of judgment by a trained
operator or observer. In another embodiment, selection of the
training set may, be achieved partially or entirely automatically
using an analysis technique such as cluster analysis. In one
example of the first approach (selection by a trained observer),
the step can be carried out by displaying one of the eight original
images to the operator on a display screen of a computer 14 (FIG.
1). The operator, based on training or experience, views the images
and determines which portions are to be used as training set, for
example, by recognizing, a portion which appears to be a primary
tumor under investigation. The operator then positions a
variable-sized box over the image portion to be selected for use as
the training set. Selection of a training set by the subjective
judgment of the skilled observer may be adequate for many
applications in medicine and industry. However, it has the
disadvantage of subjectivity.
[0075] The problem of subjectivity in set selection can be seen by
the following example of a medical application. Suppose that a
patient has a 3 cm spherical metastasis within the liver from a
primary lung carcinoma. The radiologist using the system wishes to
determine how similar the hepatic metastasis is to the known
primary lung tumor. According to the subjective method, the
radiologist would choose a training set from the known primary
tumor involving the lung, and would then use a test set that
consists of a plane passing through the hepatic metastasis.
Presumably, areas within the hepatic metastasis that were similar
to the training set in the primary lung tumor would be
identified.
[0076] The problem with this approach is that the identification of
the hepatic metastasis depends critically on the radiologist's
selection of the training set from the primary lung tumor. If the
radiologist chooses a training set that truly represents viable
tumor, then the classification should be optimal. However, if the
radiologist chooses a training set that contains both viable tumor
and nonviable necrotic tumor, then the classification will be less
accurate.
[0077] The accuracy of classification depends on how accurately the
training set represents the known tissue. If an area of normal fat
adjacent to a known tumor is unintentionally included in the
training set, the classified image will highlight both tumor and
normal fat. Likewise, if the training set contains only necrotic
tumor, viable areas of tumor in the test set will not be
identified.
[0078] In order to avoid the problem of subjectivity, one
embodiment provides an objective analysis method, such as cluster
analysis, for automatically or partially-automatically selecting a
training set to represent the material or tissue of interest.
Cluster analysis could be used to e.g., detect the inadvertent
inclusion of two distinct tissue types within a single training
set, which would alert the user to the potential problem.
[0079] Cluster analysis refers to a number of well-known
mathematical techniques for objectively classifying the components
of a data set into a plurality of classes based upon the relative
distance between various members of the data set. In many data
sets, it is found that, with respect to one or more dimensions of
measurement, certain subsets of the data are, on average, closer to
each other than other (possibly overlapping) subsets. Cluster
analysis results in an objective assignment of all or some of the
members of the data set into objectively-defined classes or
clusters.
[0080] Cluster analysis thus operates on data sets which have a
high degree of variation. Accordingly, a first step of performing
cluster analysis is to select the data set on which the cluster
analysis is to be performed. Several options are available. Cluster
analysis can be applied to one or more entire fields of view or
"slices". Alternatively, cluster analysis could be applied to only
a portion of the slice, such as that portion of the slice which
represents the body being analyzed (versus its environment). In
another case, cluster analysis could be applied to a portion of a
slice which is less than the entire slice of the body being
analyzed. However, in this case, it is useful to include, in the
cluster analysis, a relatively large region that encompasses the
substance or tissue of interest. In this context, "relatively
large" means that the region is likely to contain all types of
substance or tissue that comprise the material of interest. As one
example, if the process is to be performed for selection of a
training set that constitutes a primary lung tumor, the region
subjected to cluster analysis may include viable tumor, non-viable
necrotic tumor and non-tumor tissue. Providing a data set that
represents several tissue types permits cluster analysis to achieve
a high level of distinction in the way the clusters are defined.
If, in contrast, the cluster analysis is applied to a relatively
homogeneous region, the cluster analysis might tend to group the
data points representing the homogeneous region into clusters based
on non-significant, small variations in the homogeneous tissue.
Accordingly, it is preferred that cluster analysis be applied to a
non-homogeneous data set, i.e., a data set which is selected to
include both the material of interest and material other than that
which is of interest.
[0081] FIG. 10 depicts a process using cluster analysis according
to one embodiment of the invention. As shown in FIG. 10, a body
which will access the training sample 1002 is subjected to scanning
by an MR device, preferably using at least two different sequences
such as a T.sub.2 weighted sequence 1004a and a T.sub.1 weighted
sequence 1004b. In the depicted embodiment, an inversion recovery
90.degree. pulse is not used; this is represented by showing the
inversion time (IT) as being turned off. These sequences are used
to form a congruent set of training images 1006. The portion of the
images to be subjected to cluster analysis is selected. As noted
above, this can be all of the images or a portion of the images
1008 but preferably is a non-homogeneous set.
[0082] After the region to be submitted to cluster analysis is
selected, the data is sent to a computer 1010, and cluster analysis
is applied. Although FIGS. 10-13 depict steps performed on two or
more computers, a single computer can be used, if desired. The data
points in the region are classified into a plurality of different
groups or clusters 1012. The training set can be selected using
these clusters in several ways. First, the various clusters can be
displayed 1014 for selection by a skilled observer (e.g., by color
coding the display). The skilled observer could select 1016 all of
one cluster or a portion of a cluster to define the training set.
Selection of the training set can also be achieved objectively,
e.g., by selecting one of the clusters based on other measurements
(such as near-infrared and mass spectrometers).
[0083] Once the training set has been selected, the training set
samples are scaled 1018 in step 86. Scaling is a conventional
pattern recognition procedure in which, for example, the data
intensity values are linearly adjusted such that they have zero
mean value and a standard deviation of unity. The training set may
also be standardized 1020 in step 86. Standardization is a
technique for correcting for the drift of an MRI instrument over
time, or for differences between different MRI instruments, and is
further described below.
[0084] Still referring to FIG. 4, steps 90-96 perform a series of
steps analogous to steps 80-86, to create a test set comprising a
congruent set 1022 of e.g., 24 images of the test region of the
patient's body to be scanned for secondary tumor. In step 90, a
second set of congruent second images of the test region are
combined. The second images are obtained using the same MRI pulse
sequences, i.e., the same operator adjustable parameters, as the
first images obtained in step 80. In step 92, the second images are
each subject to the spatial correlation procedure described above
and illustrated in FIGS. 5 and 6. In step 94, the test set is
selected. This selection can be done by a skilled observer or using
cluster analysis to aid in selection 1024, as described above for
the training set. In many cases, the test set will be the complete
second images. However, in certain cases, to save processing time,
it may be desirable to specify a subregion that includes the actual
target of the investigation. Finally, in step 96, the test set is
scaled (and standardized) 1018, 1020 in a manner similar to that
performed in step 86.
[0085] Once the training and test sets have been prepared, they are
then compared to one another 1030 in step 100, in order to
determine the relative "distance" between the training set and each
member of the test set. A number of known statistical techniques
are available for computing the distance between pairs of pixels in
a multidimensional data space. For the purpose of the present
invention, however, the preferred technique has been determined to
be a simple Euclidian distance, computed as follows: 1 d = [ 1 - 1
N ( R i - S i ) 2 ] 1 / 4 ( 2 )
[0086] R.sub.i represents the ith coordinate of the training
sample, S.sub.i represents the ith coordinate of the test sample,
and N is the total number of dimensions (e.g., 24) in each data
set. Two preferred techniques have previously been described for
associating a distance value with each test set sample. In the
first technique, an average training set sample is calculated, and
the distance between each test set sample and the average training
set sample is determined. In the second technique, for each test
set sample, the distance from the test set sample to each training
set sample is measured, and the minimum of these distances is
selected. However, it will be understood that other measures of
similarity could also be used without departing from the spirit of
the present invention.
[0087] The distance measurement of Equation 2 above is an example
of the so-called KNN method (K nearest neighbor) for the case of
K=1. It is equivalent to the Euclidean distance between samples in
a multidimensional measurement space in which each dimension
corresponds to one of the images. This embodiment of the KNN
technique is an example of supervised classification using a
nonparametric classification algorithm. In parametric methods,
there are a priori choices that must be made by the user, leading
to the possibility that the classification will reflect observer
bias. A potential limitation of nonparametric methods is that they
cannot recognize outliers in the data. However, in many cases this
limitation is overcome in practice, because the human observer will
be able to consider the results of classification in the context of
the entire image, i.e., the observer serves to recognize
outliers.
[0088] Nevertheless, in some embodiments of the invention,
parametric methods (or non-parametric methods other than the KNN
method) are used for determining similarity values. An example of a
parametric method that can be used is the simple independent
modeling by class analogy (SIMCA) method, for example, in Mohammad
A. Sharaf, et al, Chemometrics, pps. 242-254, 1986, incorporated
herein by reference.
[0089] Computing the Euclidean distance between the average value
of the samples in the training set and a given sample in the test
set is computationally fast, but has the disadvantages of requiring
selection of a sample out of the test set and of providing little
information about the heterogeneity of the training set. Tissue
heterogeneity is more accurately expressed by measuring the
distance between each sample of the training set and a given sample
in the test set, and selecting the smallest distance as the
representative distance. The minimum distance measured in this way
represents the sample in the training set that is most similar to
the sample in the test set.
[0090] Once cluster analysis is performed, the results of the
cluster analysis can be used for analyzing similarities in a number
of ways. If, in the example above, a clinician wishes to know how
similar the viable tumor in the lung lesion is to the viable tumor
in the hepatic metastasis, cluster analysis can be performed in the
following way to give a numerical estimate of similarity. A skilled
observer would choose a relatively large region from the lung tumor
and would choose a relatively large region from the hepatic
metastasis. Cluster analysis would then be independently applied
1012, 1024 to each of these regions.
[0091] For the sake of this example, let us suppose that each of
the two lesions yields three clusters 1012, 1024. The results of
cluster analysis could be used in at least two ways.
[0092] In the first method, illustrated in FIG. 10, each of the
three clusters for the lung lesion could be used to classify the
entire lung lesion, and a trained observer would then look at the
three classified images and choose 1016 the one image that the
observer felt was most likely to represent viable tumor. This
process could be repeated for the liver lesion, and the chosen
regions 1026 in the liver and lung could then be compared
numerically to determine the degree of similarity, using, e.g., the
euclidean distance 1030 or other techniques described above.
[0093] Alternatively, and more objectively, as depicted in FIG. 11,
the three classes identified in the liver and lung lesions could be
arranged into a 3.times.3 matrix, and the similarities (distances)
between each pair of clusters 1102 could be determined. The pairs
of clusters having the shortest distances could then be identified
1104, and these pairs of clusters could then be indicated with a
false-color overlay on the images of the lung lesion and the
hepatic lesion 1106. The purpose of doing this identification would
be to determine which of the pairs was most likely to represent
similar, viable tumor.
[0094] The accuracy of the pattern recognition technique of the
present invention depends on the discriminating variances of the
training and test sets; the greater the discriminating variance of
the data, the greater the likelihood that two different tissue
types will be distinguished. The discriminating variance can be
increased by increasing the number of different pulse sequences
(images) applied to the region of interest. In theory, the accuracy
of classification can be made arbitrarily high by increasing the
number of sequences used. In practice, the need for greater
accuracy must be balanced by the requirement that the data not be
excessively overdetermined, and by practical limits on imaging
time. Using excessively overdetermined data reduces the ability of
the classification to generalize the properties of the training act
to identify similar, but not identical, samples; using undetermined
data for classification will lead to a large degree of nonspecific
identification.
[0095] In a medical application, it is believed that high
classification accuracy is reached using relatively low spatial
resolution for the ME-6 pulse sequence, which helps decrease the
total imaging time. Using this sequence with a 64.times.256 pixel
array (phase, frequency) leads to greater classification accuracy
than an array having a higher spatial resolution (128.times.256),
because decreasing the spatial resolution increases the pixel size,
which improves the signal-to-noise ratio. This amounts to trading
spatial resolution to gain greater spectral resolution, which
represents greater information content per pixel. This departs from
the traditional approach in MRI, which strives above all else to
achieve high spatial resolution. As hardware and software have
improved, it has become possible to increase spatial resolution
without sacrificing overall information content to an unacceptable
degree.
[0096] The degree of tissue discrimination achieved by the
invention depends on the percentage of nearest distances that are
highlighted. Highlighting a very small percentage (e.g., 0.2% to 2%
of the test samples) results in high discrimination, but lowers the
sensitivity for detecting unsuspected lesions. Highlighting a
larger percentage (2% to 8%) will decrease the degree of tissue
discrimination, but will increase the likelihood of detecting
unsuspected lesions. If the principal purpose of using MRI is to
characterize a recognized lesion of unknown origin rather than to
detect unsuspected lesions, then it is generally preferable to
highlight only the nearest 0.5% to 2% of the pixels in the test
image, to maximize tissue discrimination.
[0097] In carrying out the present invention, the data should
preferably be adequately overdetermined, such that the ratio of the
number of samples to the number of variables describing each sample
is at least three. Each sample that represents the combined ME-6,
STIR, and T.sub.1-weighted sequences consists of 8 original data
and 16 derived data that represent spatial correlation variables. A
training set that contains 24 or more samples will result in a
system that is adequately overdetermined with respect to the
original 8 data acquired for each sample.
[0098] Even though it is theoretically important to have the system
adequately overdetermined to avoid spurious correlations (i.e.,
those that arise by chance), we have found that the number of
samples included in the training set has surprisingly little effect
on the accuracy of classification. Although a training set with 4
samples is relatively undetermined, it can result in classification
that is similar to the classification achieved by a training set
consisting of 25 to 50 samples. At the other extreme, a training
set containing 700 samples decreased the amount of nonspecific
highlighting compared to a 25-sample training set. However, the 700
sample set required about 25 times more computer time than the 25
sample set. In general, we find that a training set size of 16 to
25 samples balances the classification accuracy and computational
burden.
[0099] The additional imaging time required will depend on the
operator's approach. For example, in medical applications, if
radiologists rely on combinations of T.sub.1-weighted and
T.sub.2-weighted images for evaluation of body and CNS metastases,
the time required to obtain one or more STIR sequences and multiple
spin-echo sequences may be impractical. However, because much body
and spinal oncology imaging is accomplished with a combination of
STIR and T.sub.1-weighted spin-echo sequences, acquiring a
multiple-echo spin-echo sequence at two selected anatomic sections
adds less than seven minutes to the overall imaging time, when a
relatively low spatial resolution is used for the ME-6 pulse
sequence.
[0100] A similarity image also has value in its own right. An
example of this is the creation of a similarity image where the
pixel brightness is proportional to the similarity of the pixel to
the silicone gel found in some types of breast implants.
[0101] An example of tissue (or substance) specific imaging would
be where a physician wishes to determine whether or not leakage has
occurred from a silicone breast implant. It may be known, e.g.,
that a fluid collection may represent a collection of
non-silicone-containing fluid that arises following surgery, or a
collection of silicone that has leaked from the breast implant.
[0102] To distinguish between these possibilities, a similarity
image could be created using a training set taken from the center
of the silicone-containing breast implant. The resulting similarity
image would then display pixels most similar to silicone brightest,
and those pixels least similar to silicone with the lowest signal
intensity. If the fluid external to the prosthesis capsule was
bright, then it would be presumed similar to the silicone in the
breast implant, and one could conclude that leakage had occurred.
If the fluid was relatively dark, then one would lean toward the
collection being a non-silicone-containing postoperative or
inflammatory collection.
[0103] Much of the discussion above has concentrated on problems of
classifying materials or tissues, i.e., determining whether a
material or tissue of interest is or is not of a predetermined type
(e.g., is a tissue from a lesion similar to a primary tumor; is a
tissue of interest, tumor tissue, cyst tissue, or fat tissue; is an
unknown oil, high viscosity oil or low viscosity oil). An
embodiment of the present invention can also be used to estimate or
predict the value of a continuous variable as opposed to
categorizing a tissue or substance into a finite number of discrete
classes. For example, the present invention can be used to estimate
or predict the concentration of a substance, the viscosity of a
material and the like. Furthermore, the present invention can be
used to evaluate (either into discrete classes or by assignment of
a value along a continuum) characteristics of materials which may
have complex physiochemical characteristics that may not have a
well-defined measurement scale (such as smoothness, creaminess,
greasiness).
[0104] Two general approaches for the prediction of continuous
properties based on MRI signatures can be used, generally described
as first order methods and higher-order methods. First order
methods generally involve forming a calibration curve based on a
series of samples that have a known, varying characteristic, such
as a varying concentration of a substance of interest. FIG. 12
depicts, schematically, a first order method. As shown in FIG. 12,
multiple samples having various values of a characteristic of
interest 1202, 1204, 1206 are each subjected to an MR analysis
using a certain set of sequences 1005a, 1005b. As an example, the
calibration samples, 1202, 1204, 1206 may be a series of gels
containing a surfactant whose concentration is known to be 10% in
sample 1202, 50% in sample 1204, and 90% in sample 1206. For each
of the samples, the output from the MR device is used to form a
congruent image, select a training set, and standardize the
training set, if necessary, as described above. The result is three
standardized training sets 1208, 1210, 1212. Each training set is
paired with the corresponding surfactant concentration and the
pairs of values are analyzed by a computer-implemented 1214
analysis method which is suitable for continuous property
prediction. Examples of suitable methods include partial-least
squares (PLS), principal components regression (PCR), locally
weighted regression (LWR), projection pursuit regression (PPR),
alternating conditional expectations (ACE), multivariate adaptive
regression splines (MARS), and neural networks (NN), as described,
for example in S. Sekulic, et al., "Non-Linear Mutlivariate
Calibration Methods in Analytical Chemistry," Analytical Chemistry,
65:(19) 835A-845A, Oct. 1, 1993, incorporated herein by reference.
The output of these techniques is a prediction model or mapping
1220. The mapping maps a first set of values, such as values
representing MR measurements to a second set of values such as
values representing, in the present example, the concentration of
the surfactant. The mapping is continuous over at least one domain
1224 of the second set of values. In a typical situation, the MR
signature can involve multiple dimensions (such as mean and
standard deviation values for each of a plurality of MR sequences).
For purposes of providing a simplified illustration, the x-axis of
the model 1220 depicted in FIG. 12 represents average intensity for
a T.sub.1 weighted MR measurement normalized to a scale of 0.1 to
1.0. In the example depicted in FIG. 12, the model 1220 is the
result of a curve-fitting procedure applied to the three pairs of
data "points" 1222, 1224, 1226. For the sake of simplicity, FIG. 12
depicts the model as a Cartesian graph. In fact, the model can be
characterized and stored in memory 1222, as a plurality of numbers,
e.g. a slope and intercept for a linear model, or a series of
coefficients for a non-linear model.
[0105] After the model is defined, the next step is to obtain an MR
response function of a material of unknown characteristics or
identity 1230. In the present example, this may be a gel with an
unknown concentration of surfactant. The MR response function is
generated using the same set of sequences 1205a', 1205b' that were
used to create the model 1020. The MR signature may be
standardized, as described herein, as necessary. The calibration
model 1020 is recalled 1232 from computer memory 1222 and used to
estimate the unknown concentration of surfactant based on the MR
measurements of the unknown 1230. In the simplified example,
assuming the MR measurements of the unknown 1030 yielded an average
of T.sub.1 weighted intensity of 0.3 1234, the model 1220 predicts
a surfactant concentration of about 20% 1236 which is then output
or stored 1238.
[0106] Multivariate techniques for predicting continuous properties
(such as PLS and PCR) can be used to predict values of multiple
variables simultaneously for a given sample. For example, as shown
in FIG. 12, multivariate techniques can be applied to the same set
of data 1008, 1010, 1012 to yield a second model 1240. As one
example, model 1240 may be a prediction of the viscosity of the
gel. Thus, the same MR measurements may be used to estimate or
predict 1242 both surfactant concentration and viscosity.
[0107] There are occasions when one wishes to predict a continuous
variable, but one does not have a set of calibration standards for
which the property of interest varies in a known way, and when the
composition of the sample is also unknown. For this type of problem
in continuous property prediction, second order (or higher-order)
techniques can be applied, as illustrated in FIG. 13.
[0108] The methods that are used to accomplish second order
calibration include rank annihilation (RA) as described, e.g., in
B. Wilson, "An Improved Algorithm for the Generalized Rank
Annihilation Method," Journal of Chemometrics, Vol. 3, pages
493-498, incorporated herein by reference, and bootstrap techniques
such as Bootstrap error-adjusted single-sample technique (BEST). As
described, e.g., in "Making your BEST case," Analytical Chemistry,
Vol. 65, No. 9, incorporated by reference. An important difference
between second order (RA, BEST) and first order (PLS, PCR) methods
is that the second order methods require input data that are a
function of two independent variables U and V in contrast to first
order methods, which require that the input data is a function of a
single independent variable.
[0109] To access a continuous property of a substance using a
second order calibration method, one would obtain MRI signatures of
samples using sequences of two independent variables, U and V,
where U and V are stepped through a range of values. Typically, U
and V values will be stepped-through for each of a plurality of
points within the body being subjected to analysis. For example, U
and V 1302, 1304 might each be sampled at eight different values at
each point, which would characterize the substance by a square
matrix of 64 intensities at each point. This 8.times.8 matrix would
serve as input to the second order calibration methods. The result
of this second order calibration method is a second order mapping
1320. This is depicted in FIG. 13 as a two-dimensional surface. In
general, one such surface will be generated for each point in
three-dimensional space sampled by the device. After the second
order mapping 1320 is constructed and, preferably stored in
computer memory 1222, a continuous property of an unknown 1330, is
predicted by subjecting the unknown 1330 to the same MR sequences
1205a', 1205b' used to generate the model 1320. The two independent
variables U and V 1332, 1334 that result from this MR measurement
are applied to the model 1320 to predict an estimated value 1336 of
a property of the unknown 1330.
[0110] A number of variables can be used as U and V. An example is
provided by the correlation spectroscopy ("COSY") technique,
described in e.g., J. W. Akitt, NMR and Chemistry, pps.
207-215:1992, which is typically performed in the frequency domain
and yields a 2-D spectrum whereas the present technique will
typically yield a n-D graph of intensities (2-D in the illustrated
embodiment).
[0111] Quantitative methods can be applied to medicine as well as
industry. For example, a first order technique could be used to
measure the concentration of fat or iron within the liver, oxygen
tension within a region of radiated tumor, or cellularity of the
bone marrow. A second order calibration method could be used to
measure the concentration of a biological substance in the presence
of unknown interfering contaminants. It should be recognized that
prediction of continuous properties can be made for properties that
are themselves poorly defined, such as "degree of radiation-induced
tissue damage, degree of ischemia, degree of response to
therapy."
[0112] When a second order calibration method is used, it is
preferable also to use a second-order standardization method as
described, e.g., in Eugenio Sanchez, et al., "Tensorial
Calibration," Journal of Chemometrics, Vol. 2, pages 265-280, 1988
and Yongdong Wang, et al., "Standardization of Second-Order
Instruments," Analytical Chemistry, Vol. 65, pages 1174-1180, 1993,
both incorporated by reference.
[0113] The signatures of biological tissues or inert substances can
be collected together to form a library of signatures. The concept
of a library would allow signatures of related conditions to be
grouped together into a diagnostic package. For example, consider a
patient who has undergone mastectomy for breast cancer and is
thought to be cured. A number of years after the mastectomy, the
patient develops abdominal pain, and an ultrasound examination of
the abdomen shows a 2 cm liver lesion. Because the primary tumor
was removed at the time of the mastectomy, the primary tumor is not
available for generating a MRI signature. In this circumstance, a
MRI response function of the patient's liver lesion likely obtained
with the assistance of cluster analysis, would be compared to a
library of known liver lesions to determine what the most likely
possibility would be. In one example, the library would likely
contain perhaps 100 standardized signatures of metastatic breast
cancer, 100 standardized signatures of benign hepatic hemangiomas,
100 standardized signatures of abscess, and so on.
[0114] The same approach could be taken with diffuse abnormalities
of the liver or other organs and with industrial analytical
problems, process control and biotechnology.
[0115] A number of algorithms can be used to effect comparison;
including SIMCA (Simple Independent Modelling by Class Analogy),
and K-nearest neighbor.
[0116] The MR signature response function consists of a collection
of variables that describe how a substance responds to a given set
of applied pulse sequences. As we have discussed, the MR response
function can be used for predicting class membership or for
predicting continuous properties. It is possible to combine the MRI
signature of a substance with other non-MRI measurements that
characterize the substance; to form an expanded signature. In the
setting of a library for diagnosing hepatic disorders, an expanded
signature might consist of the MRI signature of the patient's liver
tissue and variables that describe various biochemical tests made
on the patient's blood, such as serum bilirubin, amino transferase,
and so on.
[0117] The expanded signature could then be used to improve on the
diagnostic specificity of the conventional MRI signature. The same
principles of the expanded signature can be applied in
biotechnology and industry. It should be recognized that a given
library for medicine or industry could contain both MRI signatures
and expanded signatures; if unknown samples were characterized by
only their MRI signatures, then only the MRI portion of the
expanded signature would be used. It is possible to use only part
of the expanded signature, because the signature consists of a
number of variables whose identity and origin is known, and so it
is possible to distinguish variables representing the MRI signature
from variables that reflect measurements of non-MRI properties.
[0118] The properties of MR imaging allow an instrument to acquire
numerous contiguous slices simultaneously to characterize a volume
of tissue or other substance. For example, in the context of class
recognition techniques it is possible to determine the number of
pixels in an image that are similar to a chosen training set
(assuming a given level of confidence or a threshold for
determining class membership). Each pixel corresponds to a known
volume ("voxel"), which is defined by the slice thickness and the
spatial resolution of the slice. The volume of tissue in the slice
that is similar to the training set is determined by multiplying
the number of voxels similar to the training set by the volume of
the voxels. This process can be repeated automatically for
continuous slices within the imaged volume, and a 3-dimensional
estimate of volume of a targeted substance can be obtained. As an
example, the targeted substance may be the volume of viable tumor
in a hepatic metastasis, the amount of grey matter in the spinal
cord, or the volume of a chosen substance within an inhomogeneous
mixture (for example, the volume of chocolate sauce in 1/2 gallon
of marble ice cream).
[0119] The most accurate classification occurs when the test and
training sets are both acquired in parallel planes; namely, if the
training set is acquired in the coronal plane, the test set should
be acquired in the coronal plane. The training and test sets should
be acquired in parallel planes because the pixels in a given image
are not isotropic. When the training and test sets are acquired at
different times, as shown in FIG. 3C, then the standardization
technique described below should be used, to minimize effects
caused by instrumental drift. In all cases, the corresponding
sequences used to produce the training and test sets should be
acquired using identical instrument parameters: identical
phase-encoding direction, slice thickness, field of view, averages,
STIR inversion time, and TR. Preferably, the training and test sets
should be acquired on the same instrument. However, if they are
acquired on different instruments, standardization techniques can
be used to minimize the effects of different instrument responses,
as described.
[0120] Incorrect identification of pixels in the test set can occur
under at least two circumstances: first, when the discriminating
variance of the data is insufficient to enable a classification
method to distinguish between, e.g., tumor and an unrelated tissue;
second, when there is a violation of the basic assumption that the
MR signatures of tissues depend only on type of tissue and not on
the location of the tissue within the imaged plane. Conditions that
violate this assumption are: motion artifact along the direction of
the phase-encoding gradient; inhomogeneity of the gradients;
poorly-shaped radio frequency pulses; and truncation artifact and
chemical shift artifact occurring at the boundary between tissues
that have substantial difference in their MR signal intensity, such
as at the border between solid organs and mesenteric fat.
[0121] In evaluating the accuracy of the method, it is important to
distinguish between the diagnostic questions which the method has
the potential to solve, and those questions that the method is
incapable of solving. One embodiment of the invention measures the
similarity between different tissues, but generally cannot
characterize a tissue as benign or malignant, or as infected or
sterile. The user is obligated to apply the invention in a
clinically valid way, because the procedure will generate a matrix
of distances from any combination of training set and test set. The
method is meant to complement, not replace, percutaneous
biopsy.
[0122] As previously described in connection with FIG. 3C, in one
application, the present invention produces the training and test
sets from images formed at different times. However, when the
training and test set samples are produced at different times, it
is possible that drift in the response of the MRI instrument could
produce differences between the training and test samples that
would influence the results of the present method. In addition, in
certain cases, it may be necessary to acquire the training and test
samples using different MRI instruments. In this case, differences
between the response of the two instruments could affect the
distances between samples in a way not related to the similarity of
the underlying tissue.
[0123] To eliminate or at least minimize these effects,
multivariate instrument standardization techniques are preferably
used to limit errors due to instrument variation. Suitable
techniques are described in the article by Wang, Veltkamp and
Kowalski, "Multivariate Instrument Standardization," Analytical
Chemistry, 63:2750-56, hereby incorporated by reference. Of the
techniques described by Wang et al., the preferred technique is the
"direct" technique (including the piecewise direct) in which the
samples produced during one imaging session are corrected to
produce estimates of the samples that would have been produced
during the other imaging session. Because there will typically be
more test samples than training samples, it may be preferable in
terms of computer time to correct the training samples, which will
typically be acquired during the first imaging session, to produce
estimates of the target samples that would have been produced at
the second imaging session, when the test samples were
acquired.
[0124] Standardization is performed by including a plurality of
reference objects in the MR imaging apparatus during each imaging
session. This can be accomplished by positioning reference objects
such that some pixels representing the reference objects appear in
each image. Alternately, the calibration standards could be
separately imaged on a periodic basis (e.g., once a day), and used
to standardize all images acquired during that day. For the purpose
of the present invention, suitable calibration standards include
water, 1 mM (millimolar) CuSO.sub.4(aq), 1:1(v:v) acetone:water,
safflower oil, mineral oil, saturated sucrose solution, 95% ethyl
alcohol, glycerin. However, other reference objects can also be
used. To produce accurate results, identical reference objects are
used during acquisition of both the training and test sets, and the
calibration standards must not have undergone substantial variation
or degradation with time. In one embodiment, the number of
reference objects is equal to the number of independently obtained
images.
[0125] In one embodiment the data is standardized before the data
has been reconstructed to form an image. In another embodiment,
standardization is applied after the acquired data have been
reconstructed to form an image.
[0126] A problem with magnetic resonance imaging is that the
magnetic field within the MR imager is not perfectly. uniform, yet
the algorithms used to reconstruct the MR data into images assume
that the field is uniform. This discrepancy creates areas of the
images that have either greater or less signal intensity than they
would if the magnetic field were perfectly uniform.
[0127] According to one embodiment, the data is corrected for the
field inhomogeneities. This embodiment requires that certain
variables be known in advance before imaging is performed; namely,
the field of view, the sequence, and the spatial location of the
plane imaged. For purposes of this disclosure, consider a single
sequence applied to a single slice. (It is understood that this
technique can be generalized to multiple slices and multiple
sequences).
[0128] Correcting the field begins by placing a uniform reference
material, such as a water-filled cylindrical phantom, within the
magnet bore. The phantom provides a uniform substance so that the
intensity of the pixels in a slice through the phantom should be
equal if the field is uniform. A suitable section is taken through
the phantom at the specified geometric location using the specified
sequence. The average value of the pixels in the resulting image is
determined. This data is then used as a basis for correcting the
field inhomogeneity. In one embodiment, the average value of the
pixel intensity is divided by the intensity of each pixel of the
image. For a pixel whose original intensity is less than that of
the average, its ratio will be greater than 1. For a pixel whose
original value is greater than the average value, its ratio will be
less than 1. This process creates a correction matrix of ratios
that will be used to correct subsequent images that are acquired at
that particular geometric location using that particular sequence
and field-of-view.
[0129] Each pixel within the patient or nonmedical image is
multiplied by the corresponding ratio from the field-correction
matrix and stored as a new corrected value. At the conclusion of
the process the image has been corrected for the known
inhomogeneities in the applied field. It is this corrected image
that would then be used as input for MRI analysis or for
conventional radiological interpretation.
[0130] Because the field inhomogeneity represents a generally fixed
property of a given magnet and its gradient coils, once the
field-correction matrix is acquired, it preferably can be used
numerous times in the future, without the need for, e.g., daily or
weekly re-acquisition. In practice, the user would acquire a
phantom for each commonly-used sequence at the typical fields of
view and store the resulting field-correction matrices in a
workstation or within the imager itself for automatic correction of
patient or nonmedical data.
[0131] A number of approaches can be used for implementing the
described procedures in software. FIG. 14 displays one approach in
which an embodiment of the invention is implemented using five
software modules, indicated as congruency 1404, training/test sets
1406, scaling 1408, distance 1410 and display 1412.
[0132] The "congruency" module prepares a set of congruent images
(slices) that will be used to create training set and test set
files. Each slice is obtained by using different MRI pulse
sequences and comprises a two dimensional rectangular array of
pixels. (E.g., a rectangular array of dimension MDR.times.MDC where
MDR=256, and MDC-256). Each pixel is represented by a 12 bit
non-negative (unsigned) integer. In addition to the original
congruent slices, the congruency module 1404 also allows the user
the options to apply standardization and texture analysis
techniques such as, spatial correlation processing, to each pixel
in the slices. Options includes adding images of the averages of
the first nearest neighboring pixels (8), next nearest neighbors
(16), and the next next nearest neighbors (24), or other texture
analysis techniques.
[0133] The "training/test sets" module 1406 displays slices in a
gray scale to adequately show the regions of interest (e.g.,
primary tumor). The module reads in data files (congruent image
files) that were prepared by the congruency module 1404. It then
prompts the user to select which slice to display and the base and
roof values of pixel intensity for the gray scale display. The gray
scale is a color map of 256 colors with the lowest index displays
as black and the highest white. Any pixel in the selected slice
that has intensity less than the base will be displayed as black
and any pixel with intensity greater than the roof will be
displayed as white. The scheme for providing a gray scale plot can
be expressed as:
[0134] pixel_color=
[0135] 255 if pixel_intensity>roof;
[0136] 255*(pixel_intensity-base)/(roof-base);
[0137] 0 if pixel_intensity<base;
[0138] The next step is to select training set (test set) classes.
A training set class is essentially a region of samples within the
set of congruent images. There are many ways to select training set
(test set) classes. The module displays a variable-sized "rubber
band" box over the region of user's consideration. Once a region is
selected, the user will be asked to enter a class number (integer).
This class number will be used to identify different selected
regions (boxes). It is acceptable to use a single class number to
identify multiple selected regions (boxes). That is to say, a class
can contain samples in different selected boxes. After the user has
finished selecting the desired regions, the module stores the
resulting regions to a file (training/test set file). This output
file contains information that records the position of each sample
in every selected class of the congruent images. These files will
be used to calculate means and standard deviations of samples and
to compute distances.
[0139] The "scaling" module 1408 can use any of a variety of
scaling procedures, the scaling module is used to calculate the
mean and standard deviation of samples in a training/test set class
which will then be used to autoscale samples in any training/test
set class of interest. In addition to computing the mean and
standard deviation of one training/test set class in a
training/test set file, this module also calculates the mean and
standard deviation of samples in:
[0140] 1. Combinations of multiple classes in one training/test set
file
[0141] 2. Combinations of multiple classes in more than one
training/test set file.
[0142] The module allows the user the option to standardize
training/test set class samples before computing the mean and
standard deviation. It stores the results in scale files that will
be used to autoscale samples.
[0143] The "distance" module 1410 computes the "distance" between a
training set class and each member of the test set class. The
program prompts the user to submit training set file and test set
file. It then asks the user to choose the training set class and
the test set class in each file that will be used for computing
distances. The user is given the option to standardize the training
set data. User can also choose the value of K in the KNN pattern
precognition method. Next, the module prompts the user to submit
scale files (created by the "scaling" module) that will be used to
autoscale the training set and the test set classes. The same scale
file can be used to autoscale both classes. Once the training set
and test set class are autoscaled, this module computes the
distance between each pixel in the test set class and the whole
training set class. Each distance is a double precision floating
point number. Distances will be stored in a distance file. The
output distance file records computed distances of each test set
class sample. It also provides information of the maximum and
minimum, mean and standard deviation of the computed distances.
[0144] The "display" module 1412 is used to display similarities
between samples in test set class and training set classes. This is
done by superimposing a falsecolor color display of the computed
distances onto a gray scale plot of an appropriate image (slice).
The program reads in an appropriate data file of congruent images
(slices) and a distance file that was created by the "distance"
module. The user will be asked to select which one of the slices to
display, the base and roof values of the gray scale plot, and the
method of falsecolor display. This module only displays falsecolor
images of pixels in the test set class. Any pixel outside of the
test set class will be displayed as black.
[0145] Two methods can be used to display falsecolor color
image:
[0146] 1. Select percentage of pixels in the test set class to
display. Also allow user the option to select a threshold value in
order to consider only those pixels in the test set class that have
distances less than the threshold value.
[0147] 2. Select x-intercept of the linear interpolation line of
color scale (see FIG. 8). Any pixels with distances greater than
the x-intercept will be displayed as black.
[0148] Pixels can be displayed with the scaled color intensity or
with the highest intensity. That is, 256 in a 1-256 falsecolor
scale). In one embodiment, yellow, green, blue, and gray scale
color scales are used.
[0149] This embodiment allows different options of display:
[0150] 1. Single image: one distance file, one data file of
congruent images, select either percentage of x-intercept
[0151] 2. Two images:
[0152] 1. One distance file, one data file of congruent images, and
combinations of x-intercepts/percentages
[0153] 2. Same as 1 with two distances file
[0154] 3. Same as 1 with two distances files and two data
files.
[0155] Fat subtraction is accomplished in this module by:
[0156] 1. Use a gray scale as the falsecolor scale
[0157] 2. Select the lowest intensity (1-256) of falsecolors that
will not be displayed.
[0158] 3. Select the scaling factor (0-1)
[0159] 4. Subtract-the superimposed falsecolor from the underlying
gray scale color.
[0160] Any pixel in the test set class with falsecolor intensity
greater than the threshold selected in (1) will be displayed with
color intensity:
[0161] pixel_color=gray_scale_color-scaling_factor*falsecolor
[0162] Optional Step:
[0163] To smooth out the effect of the subtraction, adjacent pixels
that go from no fat subtraction to fat subtraction should be
displayed with the average intensity of the two pixels.
[0164] A number of computer configurations can be used for
performing the various computer-implemented processes described
herein. In one configuration, the computer includes a
workstation-type computer such as a Sun IPC.RTM. workstation. The
workstation can operate under a number of operating systems,
preferably a UNIX-based operating system. If desired, the
workstation can be coupled by a network link, such as an ethernet
link to other workstation and/or external mass storage such as an
external tape drive. The network environment can be, for example,
X-Windows.RTM., OpenLook.RTM., or other graphical user interface.
Although the above-described configuration can be used in
connection with the described invention, the detailed
implementation of a system for use in implementing the present
invention will depend upon the general application. Some
applications may need high definition graphics output and moderate
speeds while other applications, (e.g., applications requiring a
very high throughput or a real-time output) may need very high
speed computations but moderate or no graphic interface.
Modifications or additions to the above-described configuration
which can be used in connection with the present invention include
SUN SPARC Classic.RTM. or LX Workstation.RTM., or a mainframe
system. Memory capacity can include, for example, 16 MB or more
random access memory (RAM) and mass storage such as writable
optical disks, hard-drive storage such as 200 to 400 MB hard drive
capacity. A high resolution 15 or 16 inch (or larger) color monitor
can be used for displaying the images herein. Image processing may
employ an accelerator card and associated software and output may
include black and white or color printer devices. Computing
capacity may be increased by use of, e.g., parallel processing
cards and associated software and the like.
[0165] As described above, the results of the method of the present
invention may be displayed by displaying one of the original gray
scale MR images, and by color highlighting the pixels of that image
that correspond to the most similar samples. As long as the
training and test sets are obtained from the same set of images, it
is accurate to assume that the nearest X% of samples of the test
set are truly similar to the training set. However, this assumption
is not necessarily true when the training and test sets are
obtained from different sets of images. This can be understood by
considering classification of a test set that does not contain any
of the training tissue, i.e., the tissue in the region spanned by
the training samples. Displaying the nearest 1% of distances will
highlight 1% of the test set pixels but these distances will be
significantly greater than would have been found had the test set
contained the training tissue.
[0166] To avoid this problem, one can incorporate distance as a
threshold in the display process. In this variation, the present
invention preferably identifies the X% of the pixels of the test
set that have the smallest distance. Of those samples, only those
samples that have distances less than Y are displayed, where Y is a
selected threshold. This means that if the user chooses to
highlight the most similar 2% of the pixels, and those 2% of pixels
have distances less than the threshold distance Y (also chosen by
the user), then 2% of the pixels will be highlighted. However if
some of those 2% have distances greater than the threshold, then
only a portion of the 2% will be highlighted. If none of the
nearest 2% has a distance less than Y, then no pixels will be
highlighted.
[0167] The present invention can be applied so as to permit
adjustment of-an MRI image to selectively enhance or suppress those
portions of the image resulting from a given type of tissue. For
example, in many clinical applications, a tissue in which one is
interested may be surrounded by another tissue such as fat, that
has a similar MRI brightness. However, if the two tissue types can
be distinguished using pattern recognition, then the portion of the
images corresponding to fat can be reduced in brightness, improving
the resolution of the tissue of interest.
[0168] An example of this procedure is illustrated in FIGS. 7-9.
The procedure begins, as above, by the generation of a congruent
set 120 of images that include a region of interest of a patient.
Set 120 preferably includes additional images generated by spatial
correlation, as previously described. Set 120 forms the test set,
while a small subset 122 is selected to form the training set. The
training set is selected such that the training set samples, to the
maximum extent possible, correspond only to the tissue type that
one wishes to suppress (or enhance).
[0169] The test and training set samples are compared in step 122,
in the manner described above, to produce similarity data 124
representing the distance between each test set sample and the
training set samples. In step 126, the similarity data is converted
into a similarity image. The similarity image depicts those
portions of the test set region that are similar to the training
set. Thus if the training set contains fat tissue, then the
similarity image will depict the fat in the test set region. The
similarity image may then be displayed, if the goal is to identify
other portions of the test region that are similar to the training
region.
[0170] Alternatively, the similarity image may be adjusted, as
described below, and then subtracted from one of the original
images 120, to selectively suppress the fat portions of the
original image.
[0171] A suitable technique for producing the similarity image is
diagrammed in FIG. 8. Similarity data 124 comprises a distance
value for each sample of the test set, the distance value being a
measure of the distance of the test sample from the training
samples in a multidimensional measurement space. Thus the smaller
the distance, the greater the similarity. In FIG. 8, line 130
represents the mathematical relationship used to convert a distance
value into a pixel intensity for constructing the similarity image.
For zero distance, i.e., identical samples, a maximum pixel
intensity 132 is selected. As the distance increases from zero, the
assigned pixel intensity decreases, until a cut off distance 134 is
reached. For distances equal to or grater than the cut off
distance, the pixel intensity is set to zero. In this manner, a
pixel intensity is associated with each sample, producing a
similarity image congruent with the original images in set 120.
[0172] In step 140, an intensity threshold is chosen to enable the
user to limit the subtraction to those pixels of the similarity
image that are most similar to the training set. In step 142, the
pixels of the similarity image that are greater than the threshold
are "scaled", preferably by a user-supplied scaling factor between
zero and 1. Thus each pixel intensity in the similarity image that
is greater than the threshold is multiplied by the scaling factor.
The adjusted similarity image, represented by line 144, is then
subtracted from one of the original images, represented by line
144, to produce an adjusted image 148 that is displayed. The
overall effect of the process is that for samples having a pattern
or signature similar to the pixels in training set 122, the
intensity is reduced in the adjusted image. The amount of reduction
is controlled by the scaling factor applied in step 142. A similar
procedure can be used to produce enhancement of selected tissue
types.
[0173] An example of the image adjustment process shown in FIGS. 7
and 8 is illustrated in FIGS. 9A and 9B. FIG. 9A shows a
conventional T.sub.1-weighted MR image through a patient's head.
The region behind each eye contains optic nerves and surrounding
fat. The fat tends to obscure the optic nerves and would very
likely obscure a contrast-enhanced tumor of the optic nerve because
both fat and contrast-enhanced tumor have approximately the same
intensity. The congruent images for this application were generated
by standard T.sub.1-weighted and T.sub.2-weighted spin-echo
sequences. In this case, training set 150 was selected from a
region that included fat but not optic nerves. This training set
was used to construct a similarity image which was then subtracted
from the original image, producing the adjusted image shown in FIG.
9B. Subtraction of the fat portions of the image enables much
clearer resolution of the optic nerves themselves.
EXPERIMENTAL
Common Household Liquids
[0174] Six 20 cc samples of six different common household liquids
were placed in a plastic specimen cup. The six liquids included a
commercial hair conditioner, vinegar, cooking oil, a pipe
de-clogging composition sold under the tradename LIQUID
PLUMMER.RTM., a saturated sucrose solution, and hair shampoo. The
specimen cups were placed in a water bath in the configuration
shown in FIG. 15A. The intention was to minimize truncation
artifact. The water bath containing the samples was scanned using
the following sequences: ME-6, STIR, T.sub.1-weighted spin-echo.
Six training sets were selected, using the techniques described
herein, from the interior of each of the six images. Each of the
training sets was used to classify a scan through the six samples,
using the techniques described herein. The results are depicted in
FIGS. 15B-15F, where crosshatching is used to indicate regions that
were color-highlighted in the displays. The training sets which
produced each of FIGS. 15B-15F are indicated in Table I. As seen in
the Figures, the technique successfully identified each liquid,
except for the LIQUID PLUMMER.RTM..
1 TABLE I Figure Selected Training Set 15B Shampoo 15C Vinegar 15D
Sucrose Solution 15E Cooking Oil 15F Hair Conditioner
Whole Apples
[0175] A similar procedures was used for distinguishing among eight
unsliced apples consisting of two winter bananas, two Romes, one
Granny Smith, one Empire, one green Criterion, and one red
Criterion, set out in the configuration depicted in FIG. 16A. The
sequences used were: ME-6, STIR (inversion time 100 milliseconds
and 200 milliseconds) and T.sub.1 weighted spin-echo. Two different
classes of training sets were defined, one class near the core and
one class near the periphery of the single apple near the center.
FIG. 16B depicts, with lighter grey shades, the portion classified
with the Core training set. FIG. 16C depicts, with lighter grey
shades, the portion classified with the periphery training set. As
seen in FIGS. 16B and 16C, the algorithm was able to generalize
properties of the core versus periphery training sets to identify
core and periphery regions in other varieties of apples.
Red and White Wines
[0176] Procedures similar to those described above were used for
analyzing seven wines in unopened bottles arranged as shown in FIG.
17A. Percentages indicate alcohol content. In this case, a water
bath was not used. The sequences used include: ME-6, STIR
(inversion time 100 milliseconds), T.sub.1 weighted spin-echo and
two field-echo sequences. A first training set was defined using
the image from the red wine zinfandel. Another training set was
defined based on the white wine Johannesburg Reisling. FIG. 17B
depicts, with vertical crosshatching, the portion of the image
classified with the first training set and, with diagonal
crosshatching, the portion classified with the second training set.
As seen in FIG. 17B the method was able to generalize the
properties of red wines and identify two other types of red wines
(Pinot Noir, Beaujolais). The method was also able to generalize
the properties of the white wine to identify three other but
different white wines, chardonnay, blush reisling, and retsina.
Surfactants
[0177] This experiment was performed using two different complex
mixtures of surfactants, A and B, where compound A consists of a
mixture of two or more different commercial surfactants, and
Compound B consists of a mixture of two or more commercial
surfactants. The surfactants present in Compound A are different
than those found in Compound B.
[0178] The calibration set consisted of seven 25 cc glass vials,
each of which contained a different proportion of Compounds A and
B, ranging from 48% A to 88% A, two of the vials having the same
percentage of Compound A. A 10 mm thick MR section was taken in a
plan passing through all seven of the samples, and the following
sequences were used to develop MR signatures of the samples:
T.sub.1-weighted spin-echo, inversion recovery sequences having
inversion times of 100, 130, 160, and 200 msec, a 4-echo spin-echo
sequence, and two gradient-echo sequences. 25-pixel training sets
were taken from each of the seven samples. The test set consists of
the entire imaged region that includes all portions of the seven
samples. The training sets were scaled.
[0179] Calibration was accomplished by using the method of partial
least squares to form a 5 principal component model. Results are
seen in FIG. 18. The horizontal axis shows the percent of
surfactant blend A in the detergent samples, as established by
conventional analytic techniques. The vertical axis shows the
percent of surfactant blend A in the detergent samples as
established by the PLS model. This example represents calibration
of first-order MR data.
[0180] The calibration model could have been used to determine
the-percentage of A at each of the 30,000 pixels in the test set.
Had this step been taken, percentage of A could have been displayed
e.g. as a false color image of the test set with color proportional
to percentage of A.
[0181] While the preferred embodiment of the invention has been
illustrated and described, it will be appreciated that various
changes can be made therein without departing from the spirit and
scope of the invention.
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