U.S. patent application number 12/234910 was filed with the patent office on 2010-03-25 for device for generating alternative of normal brain database.
This patent application is currently assigned to UNIVERSITY OF WASHINGTON. Invention is credited to Shuya Miki, Satoshi Minoshima, Kazuhiro Nishikawa, Kiyotaka Watanabe.
Application Number | 20100074480 12/234910 |
Document ID | / |
Family ID | 42037713 |
Filed Date | 2010-03-25 |
United States Patent
Application |
20100074480 |
Kind Code |
A1 |
Minoshima; Satoshi ; et
al. |
March 25, 2010 |
DEVICE FOR GENERATING ALTERNATIVE OF NORMAL BRAIN DATABASE
Abstract
CPU 12 reads out the bloodstream associated values of target
voxel of subject's standardized brain bloodstream images (step
S34). CPU 12 sorts the bloodstream associated values in descending
order (step S35). CPU 12 rejects bloodstream associated values that
are ranked top 10% and bottom 40% (step S36). When the subjects are
20 for example, bloodstream associated values of highest 2 subjects
and of lowest 8 subjects are rejected. CPU 12 calculates and stores
mean value and standard deviation of remaining bloodstream
associated values after the rejection (step S37). CPU 12 calculates
mean value and standard deviation of bloodstream associated values
for each voxel as target voxel (steps S31, S32, S33 and S38). Then,
the alternative normal brain database of brain bloodstream image is
obtained.
Inventors: |
Minoshima; Satoshi;
(Seattle, WA) ; Watanabe; Kiyotaka; (Tokyo,
JP) ; Miki; Shuya; (Tokyo, JP) ; Nishikawa;
Kazuhiro; (Nishinomiya-shi, JP) |
Correspondence
Address: |
Jason H. Vick;Sheridan Ross, PC
Suite # 1200, 1560 Broadway
Denver
CO
80202
US
|
Assignee: |
UNIVERSITY OF WASHINGTON
Seattle
WA
NIHON MEDI-PHYSICS CO., LTD
Tokyo
|
Family ID: |
42037713 |
Appl. No.: |
12/234910 |
Filed: |
September 22, 2008 |
Current U.S.
Class: |
382/128 |
Current CPC
Class: |
G06T 2207/10072
20130101; G16H 50/20 20180101; G06K 9/00926 20130101; G06T
2207/30016 20130101; G16H 70/60 20180101; G06K 9/6255 20130101;
G06T 7/0014 20130101; A61B 6/037 20130101; A61B 6/507 20130101;
G16H 30/40 20180101 |
Class at
Publication: |
382/128 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A device for generating alternative of normal brain database to
be used for diagnosing brain disease based on brain status images
of subjects including patients, the alternative of normal brain
database showing normal status of each points of brain, the device
comprising: means for normalizing status value at each point of
each brain status image of subject based on status values of a part
or whole of brain status image of the subject; means for spatially
transforming each brain status image of subject in accordance with
anatomical standard brain; means for rejecting status values
presumed as indicating disease with regard to each points of brain
to be used for diagnosing disease based on said anatomically
transformed and normalized brain status images of subjects; and
means for generating the alternative of normal brain database by
obtaining at least mean status value for said each point of brain
to be used for diagnosing disease based on said anatomically
transformed and normalized brain status images of subjects in which
said status values presumed as indicating disease are rejected.
2. A device for generating alternative of normal brain database in
accordance with claim 1, wherein said rejecting means rejects
extreme deviate values statistically obtained at each point as said
status values presumed as indicating disease.
3. A device for generating alternative of normal brain database in
accordance with claim 2, wherein said brain status image is a brain
bloodstream image which shows bloodstream associated value of each
points of brain.
4. A device for generating alternative of normal brain database in
accordance with claim 3, wherein said rejecting means rejects the
smallest value to the m-th smallest as said status values presumed
as indicating disease for each points of brain to be used for
diagnosing disease based on anatomically transformed and normalized
brain status images of subjects.
5. A device for generating alternative of normal brain database in
accordance with claim 4, wherein said rejecting means further
rejects the largest value to the n-th largest for each points of
brain to be used for diagnosing disease based on anatomically
transformed and normalized brain status images of subjects.
6. A device for generating alternative of normal brain database in
accordance with claim 5, wherein the number m of rejecting the
smallest value to the m-th smallest is larger than the number n of
rejecting the largest value to the n-th largest.
7. A device for generating alternative of normal brain database in
accordance with claim 1, wherein said generating means generates
the alternative of normal brain database by obtaining mean status
value and standard deviation for said each point of brain to be
used for diagnosing disease based on said anatomically transformed
and normalized brain status images of subjects in which said status
values presumed as indicating disease are rejected.
8. A device for generating alternative of normal brain database in
accordance with claim 1, further comprising: means for generating
brain surface status image of each subjects based on said
anatomically transformed and normalized brain status images of
subjects, said brain surface status image having surface status
value of each surface portions, said surface status value being
selected as representative value of status values from brain
surface to predetermined depth perpendicular to the brain surface;
and wherein said each surface point at which said surface status
value is indicated is used as said point of brain to be used for
diagnosing disease.
9. A device for generating alternative of normal brain surface
database to be used for diagnosing disease, based on brain
bloodstream images of subjects including patients which show
bloodstream associated value of each points of brain, the device
comprising: means for normalizing bloodstream associated value at
each point of each brain bloodstream image of subject based on
bloodstream associated values of a part or whole of brain
bloodstream image of the subject; means for spatially transforming
each brain bloodstream image of subject in accordance with
anatomical standard brain; means for generating brain surface
bloodstream image of each subjects based on said anatomically
transformed and normalized brain bloodstream images of subjects,
said brain surface bloodstream image having surface bloodstream
associated value of each surface points, said surface bloodstream
associated value being selected as representative value of
bloodstream associated values from brain surface to predetermined
depth perpendicular to the brain surface; and means for generating
the alternative of normal brain surface database by obtaining mean
bloodstream associated value and standard deviation for each
surface points based on generated brain surface bloodstream image
of each subjects, said mean bloodstream associated value and
standard deviation being obtained by calculating average of
selected bloodstream associated values and standard deviations of
the surface portion, said selected bloodstream associated values
are selected from all bloodstream associated values of the surface
portion by excluding at least the smallest value to m-th
smallest.
10. A computer-readable recording medium storing program for
generating alternative of normal brain database to be used for
diagnosing brain disease based on brain status images of subjects
including patients, the alternative of normal brain database
showing normal status of each points of brain, the program
comprising instructions for the steps of: normalizing status value
at each point of each brain status image of subject based on status
values of a part or whole of brain status image of the subject;
spatially transforming each brain status image of subject in
accordance with anatomical standard brain; rejecting status values
presumed as indicating disease with regard to each points of brain
to be used for diagnosing disease based on said anatomically
transformed and normalized brain status images of subjects; and
generating the alternative of normal brain database by obtaining at
least mean status value for said each point of brain to be used for
diagnosing disease based on said anatomically transformed and
normalized brain status images of subjects in which said status
values presumed as indicating disease are rejected.
11. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 10, wherein said rejected status values in said rejecting
step are extreme deviate values statistically obtained at each
point as said status values presumed as indicating disease.
12. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 11, wherein said brain status image is a brain bloodstream
image which shows bloodstream associated value of each points of
brain.
13. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 12, wherein said rejected status values in said rejecting
step are the smallest value to the m-th smallest as said status
values presumed as indicating disease for each points of brain to
be used for diagnosing disease based on anatomically transformed
and normalized brain status images of subjects.
14. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 13, wherein the largest value to the n-th largest for each
points of brain to be used for diagnosing disease based on
anatomically transformed and normalized brain status images of
subjects are further rejected in said rejecting step.
15. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 14, wherein the number m of rejecting the smallest value to
the m-th smallest is larger than the number n of rejecting the
largest value to the n-th largest.
16. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 10, wherein in said generating step the alternative of normal
brain database is generated by obtaining mean status value and
standard deviation for said each point of brain to be used for
diagnosing disease based on said anatomically transformed and
normalized brain status images of subjects in which said status
values presumed as indicating disease are rejected.
17. A computer-readable recording medium storing program for
generating alternative of normal brain database in accordance with
claim 10, further comprising instruction for the step of:
generating brain surface status image of each subject based on said
anatomically transformed and normalized brain status images of
subjects, said brain surface status image having surface status
value of each surface portions, said surface status value being
selected as representative value of status values from brain
surface to predetermined depth perpendicular to the brain surface;
and wherein said each surface point at which said surface status
value is indicated is used as said point of brain to be used for
diagnosing disease.
18. A computer-readable recording medium storing program for
generating alternative of normal brain surface database to be used
for diagnosing disease, based on brain bloodstream images of
subjects including patients which show bloodstream associated value
of each points of brain, the program comprising instructions for
the steps of: normalizing bloodstream associated value at each
point of each brain bloodstream image of subject based on
bloodstream associated values of a part or whole of brain
bloodstream image of the subject; spatially transforming each brain
bloodstream image of subject in accordance with anatomical standard
brain; generating brain surface bloodstream image of each subjects
based on said anatomically transformed and normalized brain
bloodstream images of subjects, said brain surface bloodstream
image having surface bloodstream associated value of each surface
points, said surface bloodstream associated value being selected as
representative value of bloodstream associated values from brain
surface to predetermined depth perpendicular to the brain surface;
and generating the alternative of normal brain surface database by
obtaining mean bloodstream associated value and standard deviation
for each surface points based on generated brain surface
bloodstream image of each subjects, said mean bloodstream
associated value and standard deviation being obtained by
calculating average of selected bloodstream associated values and
standard deviations of the surface portion, said selected
bloodstream associated values are selected from all bloodstream
associated values of the surface portion by excluding at least the
smallest value to m-th smallest.
19. A method for generating alternative of normal brain database to
be used for diagnosing brain disease based on brain status images
of subjects including patients, the alternative of normal brain
database showing normal status of each points of brain, the method
comprising the steps of: normalizing status value at each point of
each brain status image of subject based on status values of a part
or whole of brain status image of the subject; spatially
transforming each brain status image of subject in accordance with
anatomical standard brain; rejecting status values presumed as
indicating disease with regard to each points of brain to be used
for diagnosing disease based on said anatomically transformed and
normalized brain status images of subjects; and generating the
alternative of normal brain database by obtaining at least mean
status value for said each point of brain to be used for diagnosing
disease based on said anatomically transformed and normalized brain
status images of subjects in which said status values presumed as
indicating disease are rejected.
20. A method for generating alternative of normal brain database in
accordance with claim 19, wherein said rejected status values in
said rejecting step are extreme deviate values statistically
obtained at each point as said status values presumed as indicating
disease.
21. A method for generating alternative of normal brain database in
accordance with claim 20, wherein said brain status image is a
brain bloodstream image which shows bloodstream associated value of
each points of brain.
22. A method for generating alternative of normal brain database in
accordance with claim 21, wherein said rejected status values in
said rejecting step are the smallest value to the m-th smallest as
said status values presumed as indicating disease for each points
of brain to be used for diagnosing disease based on anatomically
transformed and normalized brain status images of subjects.
23. A method for generating alternative of normal brain database in
accordance with claim 22, wherein the largest value to the n-th
largest for each points of brain to be used for diagnosing disease
based on anatomically transformed and normalized brain status
images of subjects are further rejected in said rejecting step.
24. A method for generating alternative of normal brain database in
accordance with claim 23, wherein the number m of rejecting the
smallest value to the m-th smallest is larger than the number n of
rejecting the largest value to the n-th largest.
25. A method for generating alternative of normal brain database in
accordance with claim 19, wherein in said generating step the
alternative of normal brain database is generated by obtaining mean
status value and standard deviation for said each point of brain to
be used for diagnosing disease based on said anatomically
transformed and normalized brain status images of subjects in which
said status values presumed as indicating disease are rejected.
26. A method for generating alternative of normal brain database in
accordance with claim 19, further comprising the step of:
generating brain surface status image of each subject based on said
anatomically transformed and normalized brain status images of
subjects, said brain surface status image having surface status
value of each surface portions, said surface status value being
selected as representative value of status values from brain
surface to predetermined depth perpendicular to the brain surface;
and wherein said each surface point at which said surface status
value is indicated is used as said point of brain to be used for
diagnosing disease.
27. A method for generating alternative of normal brain surface
database to be used for diagnosing disease, based on brain
bloodstream images of subjects including patients which show
bloodstream associated value of each points of brain, the method
comprising the steps of: normalizing bloodstream associated value
at each point of each brain bloodstream image of subject based on
bloodstream associated values of a part or whole of brain
bloodstream image of the subject; spatially transforming each brain
bloodstream image of subject in accordance with anatomical standard
brain; generating brain surface bloodstream image of each subjects
based on said anatomically transformed and normalized brain
bloodstream images of subjects, said brain surface bloodstream
image having surface bloodstream associated value of each surface
points, said surface bloodstream associated value being selected as
representative value of bloodstream associated values from brain
surface to predetermined depth perpendicular to the brain surface;
and generating the alternative of normal brain surface database by
obtaining mean bloodstream associated value and standard deviation
for each surface points based on generated brain surface
bloodstream image of each subjects, said mean bloodstream
associated value and standard deviation being obtained by
calculating average of selected bloodstream associated values and
standard deviations of the surface portion, said selected
bloodstream associated values are selected from all bloodstream
associated values of the surface portion by excluding at least the
smallest value to m-th smallest.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention relates to generate an alternative of
normal brain database (hereinafter referred as "alternative normal
brain database") which can be used for diagnosing disease based on
functional image of brain such as PET (positron emission
tomography) image and SPECT (single photon emission computed
tomography) image.
[0003] 2. Description of Related Art
[0004] To diagnose Alzheimer's disease etc., measuring and imaging
functional status such as bloodstream or glucose metabolism of each
point of patient's brain is carried out. In positron emission
tomography (hereinafter referred as "PET"), medical agent which is
indicated by positron emission nuclear such as .sup.18F-FDG is
injected to obtain the functional status such as glucose metabolism
by measuring gamma ray amount as annihilation radiation at each
point of patient's brain. In single photon emission computed
tomography (hereinafter referred as "SPECT"), gamma ray emission
nuclear species such as .sup.123I and .sup.99mTc is used for the
same purpose. As shown in FIG. 1, plural sectional images are
generated by measuring gamma ray amount of each section of
patient's brain. In the sectional image, for example, red, yellow,
green and blue are used for showing areas in descending order of
status value such as bloodstream or glucose metabolism associated
value (voxel value associating functional value measured by PET of
SPECT)
[0005] Diagnosis of disease can be carried out by comparing data
showing status value of normal healthy subject and data showing
status value of patient. For comparing, computer displays
differential image between the sectional image of normal healthy
subject and the sectional image of patient. To obtain the
differential image, the sectional image of patient is spatially
fitted to that of normal healthy subject and Z-score at each point
is calculated. The method to achieve such diagnosis is well known
such as 3-Dimentional stereotaxic surface projection (3D-SSP)
developed by the Minoshima of Washington University, and
Statistical Parametoric Mapping (SPM) developed by Friston et al.
of Hammersmith Hospital, U.K.
[0006] The data showing status value of normal healthy subject
comprises mean and standard deviation of status values of each
point which are obtained from plural normal healthy subjects. The
data showing status value of normal healthy subject are called as
normal brain database. The Z-score is obtained by dividing
difference between the status value of patient and the status value
of normal healthy subject at each point by standard deviation at
each point of normal brain database. See equation (1).
Z(x,y,z)=(I.sub.mean(x,y,z)-I(x,y,z))/SD(x,y,z) (1)
Where Z(x,y,z) is Z-score at the point of coordinate x,y,z,
I.sub.mean(x,y,z) is mean value of status values (voxel values
associated to functional status measured by PET of SPECT etc.) at
said point of normal healthy subjects, I(x,y,z) is status value at
said point of the patient and SD(x,y,z) is standard deviation of
status values at the point of normal healthy subjects.
I.sub.mean(x,y,z) and SD(x,y,z) can be obtained from the normal
brain database.
[0007] In this method, difference between normal healthy subject
and patient can be clearly shown by using the normal brain database
having standard deviation.
[0008] In 3D-SSP, the biggest status value from brain surface to
predetermined depth perpendicular to the brain surface is selected
as representative status value and is displayed on the brain
surface. Then, Z-score is calculated by comparing the selected
status values of patient with that of normal healthy subjects.
Images of Z-score are displayed as right-brain lateral surface
RT-LAT, left-brain lateral surface LT-LAT, top surface SUP, bottom
surface INF, anterior surface ANT, posterior surface POST,
right-brain medial surface R-MED and left-brain medial surface
L-MED as shown in FIG. 2. In FIG. 2, upper images (denoted
"surface") show status values of brain surface and lower images
(denoted "GLB") show Z-score of brain surface. Z-score image
enables to improve detection ability of disease and to assess
severity of disease.
[0009] Mean value and standard deviation of selected status values
of brain surface (said selected biggest values) at each point of
brain surface of plural normal healthy subjects should be provided
as normal brain database in the 3D-SSP method.
[0010] To achieve high diagnosing ability, the normal brain
database is made based on preferably at least 10 normal healthy
subjects. See Chen W P et al., "Effect of sample size for normal
brain database on diagnostic performance of brain FDG PET for the
detection of Alzheimer's disease using automated image analysis"
Nucl Med Commun. 2008 March; 29(3):270-6.
[0011] It is not easy to gather image data of normal healthy
subjects, because most of functional brain images such as PET
images and SPECT images gathered by the medical center are the
functional brain images of subjects who visit medical center and
possibly have any disease. Further, functional brain image may vary
according to radio isotopes corresponding to disease to be
diagnosed and materials indicating them. Therefore, normal brain
database should be generated for each combination of radio isotopes
and materials indicating them. Above mentioned situations disturb
the generation of normal brain database.
SUMMARY OF THE INVENTION
[0012] In the following description, "brain status image" means
image which shows status of brain such as brain functional
image.
[0013] It is an object of the present invention to provide a device
and method which can easily generate alternative of normal brain
database which can be used for alternative for the normal brain
database which is difficult to generate.
(1) A device for generating alternative of normal brain database to
be used for diagnosing brain disease based on brain status images
of subjects including patients, embodying the present invention
comprises:
[0014] means for normalizing status value at each point of each
brain status image of subject based on status values of a part or
whole of brain status image of the subject;
[0015] means for spatially transforming each brain status image of
subject in accordance with anatomical standard brain;
[0016] means for rejecting status values presumed as indicating
disease with regard to each points of brain to be used for
diagnosing disease based on said anatomically transformed and
normalized brain status images of subjects; and
[0017] means for generating the alternative of normal brain
database by obtaining at least mean status value for said each
point of brain to be used for diagnosing disease based on said
anatomically transformed and normalized brain status images of
subjects in which said status values presumed as indicating disease
are rejected.
(2) In one embodiment of the present invention, the rejecting means
rejects extreme deviate values statistically obtained at each point
as said status values presumed as indicating disease. (3) In one
embodiment of the present invention, the brain status image is a
brain bloodstream image which shows bloodstream associated value
(voxel value obtained by PET or SPECT etc. associated with the
bloodstream value) of each points of brain. (4) In one embodiment
of the present invention, the rejecting means rejects the smallest
value to the m-th smallest as said status values presumed as
indicating disease for each points of brain to be used for
diagnosing disease based on anatomically transformed and normalized
brain status images of subjects. (5) In one embodiment of the
present invention, the rejecting means further rejects the largest
value to the n-th largest for each points of brain to be used for
diagnosing disease based on anatomically transformed and normalized
brain status images of subjects. (6) In one embodiment of the
present invention, the number m of rejecting the smallest value to
the m-th smallest is larger than the number n of rejecting the
largest value to the n-th largest. (7) In one embodiment of the
present invention, the generating means generates the alternative
of normal brain database by obtaining mean status value and
standard deviation for said each point of brain to be used for
diagnosing disease based on said anatomically transformed and
normalized brain status images of subjects in which said status
values presumed as indicating disease are rejected. (8) A device
for generating alternative of normal brain database embodying the
present invention further comprises:
[0018] means for generating brain surface status image of each
subjects based on said anatomically transformed and normalized
brain status images of subjects, said brain surface status image
having surface status value of each surface portions, said surface
status value being selected as representative value of status
values from brain surface to predetermined depth perpendicular to
the brain surface; and
[0019] wherein said each surface point at which said surface status
value is indicated is used as said point of brain to be used for
diagnosing disease.
[0020] Brain surface bloodstream image or brain surface glucose
metabolism image as the brain surface status image can be generated
by bloodstream values of brain bloodstream image or glucose
metabolisms of brain glucose metabolism image, respectively
(9) A device for generating alternative of normal brain surface
database to be used for diagnosing disease, based on brain
bloodstream images of subjects including patients which show
bloodstream associated value of each points of brain, embodying the
present invention comprises:
[0021] means for normalizing bloodstream associated value at each
point of each brain bloodstream image of subject based on
bloodstream associated values of a part or whole of brain
bloodstream image of the subject;
[0022] means for spatially transforming each brain bloodstream
image of subject in accordance with anatomical standard brain;
[0023] means for generating brain surface bloodstream image of each
subjects based on said anatomically transformed and normalized
brain bloodstream images of subjects, said brain surface
bloodstream image having surface bloodstream associated value of
each surface points, said surface bloodstream associated value
being selected as representative value of bloodstream associated
values from brain surface to predetermined depth perpendicular to
the brain surface; and
[0024] means for generating the alternative of normal brain surface
bloodstream database by obtaining mean bloodstream associated value
and standard deviation for each surface points based on generated
brain surface bloodstream image of each subjects, said mean
bloodstream associated value and standard deviation being obtained
by calculating average of selected bloodstream associated values
and standard deviations of the surface portion, said selected
bloodstream associated value s are selected from all bloodstream
associated values of the surface portion by excluding at least the
smallest value to m-th smallest.
[0025] The forgoing forms and other forms, objects, and aspects as
well as features and advantages of the present invention will
become further apparent from the following detailed description of
the presently preferred embodiments, read in conjunction with the
accompanying drawings. The detailed description and drawings are
merely illustrative of the present invention rather than limiting
the scope of the present invention being defined by the appended
claims and equivalents thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] FIG. 1 shows plural sectional images of brain bloodstream
associated value.
[0027] FIG. 2 shows images of brain surface bloodstream associated
value.
[0028] FIG. 3 shows a block diagram of a device for generating
alternative of normal brain database (hereinafter referred as
"alternative normal brain database").
[0029] FIG. 4 shows a block diagram of a device for generating
alternative normal brain database in another embodiment of the
present invention.
[0030] FIG. 5 shows a hardware construction of the device for
generating alternative normal brain database.
[0031] FIG. 6 is a flowchart of the program for generating
alternative normal brain database stored in hard-disk.
[0032] FIG. 7 is a detailed flowchart for obtaining brain
bloodstream data (step S1 of FIG. 6)
[0033] FIG. 8 shows normalized brain bloodstream data.
[0034] FIG. 9 shows X axis, Y axis and Z-axis relating to
cephalon.
[0035] FIGS. 10 and 11 show a detailed flowchart for standardizing
the bloodstream image (step S2 of FIG. 6).
[0036] FIGS. 12 and 13 show a detailed flowchart for identifying
the interhemispheric mid-sagittal plane of the bloodstream image
(step S22 of FIG. 10).
[0037] FIG. 14 shows calculation of the stochastic sign change
(SSC).
[0038] FIG. 15 is a detailed flowchart for identifying the AC-PC
line and the center thereof (step S23 of FIG. 10).
[0039] FIG. 16 shows steps for identifying the AC-PC line.
[0040] FIG. 17 shows estimating the landmark TH for identifying the
AC-PC line.
[0041] FIG. 18 shows liner transformation of the bloodstream image
to accord with the anatomical standard brain.
[0042] FIG. 19 shows non-liner transformation of the bloodstream
image in accordance with the anatomical standard brain.
[0043] FIG. 20 shows a detailed flowchart for generating the brain
bloodstream image (step S27 of FIG. 11).
[0044] FIGS. 21 and 22 show a detailed flowchart for generating the
alternative normal brain database (step S3 of FIG. 6).
[0045] FIG. 23 shows a temporally table for rejecting bloodstream
associated value (s) presumed as indicating disease with regard to
particular voxel.
[0046] FIG. 24 shows a part of the generated alternative normal
brain database of brain bloodstream.
[0047] FIG. 25 shows a part of the generated alternative normal
brain database of brain surface bloodstream.
[0048] FIG. 26 shows a detailed flowchart of rejecting process in
another embodiment.
[0049] FIG. 27 shows a table of T for rejecting in another
embodiment.
DETAILED DESCRIPTION OF THE INVENTION
1. Functional Block Diagram
[0050] FIG. 3 shows a functional block diagram of a device for
generating alternative normal brain database of an embodiment of
the present invention. The device is adapted to be able to generate
the alternative normal brain database based on plural brain status
images of subjects including patient(s). All of the plural brain
status images provided to the device may be patient's brain status
images.
[0051] Referring to FIG. 3, anatomical standardization means 2
obtains the brain status images having brain status values and
spatially transforms the brain status image of each subject in
accordance with anatomical standard brain such as Talairach's
standard brain so that the brain status image of each subject is
standardized by adjusting spatial differences.
[0052] Rejecting means 4 identifies the brain status values
presumed as indicating disease based on comparing the brain status
values of subjects with each other at each point of the
standardized brain images. Then the rejecting means 4 rejects the
brain status values presumed as indicating disease. This rejecting
process may be carried out based on statistical method. In one
embodiment, the brain status values of each point are sorted in
descending order and the n-th largest and/or the m-th smallest
values are rejected. The rejecting means 4 carries out the
rejecting process for all of the points.
[0053] Database generating means 6 comprises mean value calculating
means 8 and standard deviation calculating means 10. The mean value
calculating means 8 calculates mean value of remaining brain status
values after rejecting process with regard to each point. The
standard deviation calculating means 10 calculates standard
deviation of remaining brain status values after rejecting process
with regard to each point. The calculated mean value and standard
deviation is recorded associated with the information showing the
position of each point. The mean values, standard deviations and
positions constitute the alternative normal brain database.
[0054] FIG. 4 shows a functional block diagram of a device for
generating alternative normal brain database in another embodiment
of the present invention. The device generates alternative normal
brain database of brain surface status for such as
Three-Dimensional Stereotactic Surface Projection (3D-SSP)
method.
[0055] Referring to FIG. 4, anatomical standardization means 2
obtains the brain status images having brain status values and
spatially transforms the brain status image of each subject in
accordance with anatomical standard brain.
[0056] Generating means 3 generates brain surface status image that
has representative status value of each point of the brain surface
which are selected from status values near the brain surface. The
generating means 3 generates the brain surface status images of all
subjects.
[0057] Rejecting means 4 rejects the brain surface status values
presumed as indicating disease by comparing the brain surface
status values of all subjects with each other at each point of the
standardized brain status images.
[0058] Database generating means 6 comprises mean value calculating
means 8 and standard deviation calculating means 10. The mean value
calculating means 8 calculates mean value of remaining brain
surface status values after rejecting process with regard to each
point. The standard deviation calculating means 10 calculates
standard deviation of remaining brain surface status values after
rejecting process with regard to each point. The each calculated
mean value and standard deviation is recorded at each point of the
standardized brain status images. The mean values, standard
deviations and positions are comprised of the alternative normal
brain database.
2. Hardware Construction
[0059] FIG. 5 shows hardware construction of the device of FIGS. 3
and 4. Although the device realizes both the functions shown in
FIGS. 3 and 4 in following embodiment, the device may have either
function. In the following embodiment, brain bloodstream associated
value is disclosed as an example of brain status value.
[0060] CPU 12 is connected to hard-disk drive 14, CD-ROM drive 16,
display device 18 for displaying brain image etc., memory 20 and
keyboard/mouse 22. The memory 20 is used for working area of CPU
12. Keyboard/mouse 22 is for inputting instructions from user.
[0061] The hard-disk records operating system (OS) 24 such as
WINDOWS.TM., generating program 26 for alternative normal brain
database and anatomical standard brain data 28. These programs and
data are installed from recording medium such as CD-ROM 30 by using
CD-ROM drive 16. The generating program 26 fulfills its function by
cooperating with the OS 24.
3. Process by the Generating Program 26 for Normal Database
[0062] FIG. 6 is a flowchart of the generating program for
alternative normal brain database. The generating process comprises
three steps, obtaining brain bloodstream associated values and
normalizing (step S1), anatomical standardization (step S2) and
generating alternative normal brain database (step S3). Although
brain bloodstream associated value is used in the following
embodiment, other functional image (value) may be used.
3.1 Obtaining Brain Bloodstream Associated Values and Normalizing
(Step S1)
[0063] Detailed steps for obtaining and normalizing brain
bloodstream associated values are shown in FIG. 7. First, CPU 12
obtains SPECT data of plural subjects including patient(s) (step
S11). The device shown in FIG. 5 can obtain the SPECT data directly
form SPECT device when the device is connected to SPECT device
through local area network (LAN) etc. The device may obtain the
SPECT data by reading the data from recording medium on which SPECT
device records measured data (SPECT data). The SPECT data comprise
projection data obtained form the subject by using SPECT
device.
[0064] CPU 12 reconstructs three-dimensional image data which has
bloodstream associated values of, for example, 2 mm cubic vowels
based on the SPECT data (step S12).
[0065] Then, CPU 12 normalizes the voxel values, because the voxel
value of the three-dimensional image comes under the influence of
measurement condition differences including measurement device
difference. In normalizing step, CPU 12 divides the bloodstream
associated value of each point i.e. each voxel by mean value of
bloodstream associated values of entire each subject's brain (mean
value of entire brain voxels) and records the divided value as
normalized bloodstream associated value on the hard-disk 14.
[0066] Mean value of bloodstream associated values of subthalamic,
cerebella, pons or sensorimotor cortex may be used as normalizing
standard part instead of the mean value of bloodstream associated
values of entire brain in normalizing step. Subthalamic, cerebella,
pons or sensorimotor cortex from the SPECT data can be identified
by superposing the subject's image on the anatomical standard brain
image in which area of each part is predetermined. The normalizing
standard part is suitably a part where reduction of bloodstream
associated value is not observed in the disease to be
diagnosed.
[0067] FIG. 8 shows normalized data of three-dimensional
bloodstream image. In FIG. 8, column "X, Y, Z" represents position
data in three-dimensional coordinate, X indicates X-coordinate
data, Y indicates Y-coordinate data and Z indicates Z-coordinate
data. As shown in FIG. 9, X denotes horizontal direction, Y denotes
anteroposterior direction and Z denotes up and down direction of
head. The normalized bloodstream data are generated for each
subject and recorded on hard-disk 14.
3.2 Anatomical Standardization (step S2)
[0068] CPU 12 spatially transforms normalized three-dimensional
bloodstream image of each subject in accordance with anatomically
standard brain (step S2 of FIG. 6). Detailed flowchart of
anatomical standardization is shown in FIGS. 10 and 11.
3.2.1 Identifying the Interhemispheric Mid-Sagittal Plane
[0069] CPU 12 reads out the normalized 3-D bloodstream image (see
FIG. 8) of initial subject from the hard-disk 14 (step S21). Then,
CPU 12 identifies the interhemispheric mid-sagittal plane of the
normalized 3-D bloodstream image read out in step S21 (step S22).
Referring to FIG. 9, the interhemispheric mid-sagittal plane is
defined as Y-Z plane including line a which passes center of a head
in a horizontal direction: that is, a plane which equally divides
the head with regard to horizontal direction.
[0070] In this embodiment, briefly, method for identifying the
interhemispheric mid-sagittal plane is as follows:
[0071] First, center point of the normalized 3-D bloodstream image
is identified and a Y-Z plane including the center point is assumed
as the interhemispheric mid-sagittal plane. Then, the normalized
3-D bloodstream image is flipped with respect to the assumed
interhemispheric mid-sagittal plane as flipping plane. An image
which is symmetric to the assumed interhemispheric mid-sagittal
plane is generated. Similarity index between the generated plane
symmetry image and original image (the normalized 3-D bloodstream
image) is calculated.
[0072] Then, the assumed interhemispheric mid-sagittal plane is
moved along the X direction, rotated around the Z axis and rotated
around the Y axis. The similarity index is calculated for each
assumed interhemispheric mid-sagittal plane.
[0073] The interhemispheric mid-sagittal plane is identified by
selecting the assumed interhemispheric mid-sagittal plane which has
maximum similarity index, because the similarity index should be
maximum when the normalized 3-D bloodstream image is flipped at
center plane.
[0074] Detailed flowchart for identifying the interhemispheric
mid-sagittal plane is shown in FIGS. 12 and 13. CPU 12 decides
center point x.sub.0, y.sub.0, z.sub.0 of the normalized 3-D
bloodstream image by simply using coordinate position. New center
point is determined by moving the center point along X direction
with .DELTA.x (step S222).
[0075] CPU 12 assumes that original point of coordinate is as the
new center point and rotates the Y-Z plane which passes through the
center point around the Z axis with .DELTA..phi.z and around the Y
axis with .DELTA..phi.y. CPU 12 assumes the moved and rotated plane
as the interhemispheric mid-sagittal plane (step S225)
[0076] CPU 12 generates flipped normalized 3-D bloodstream image
which is symmetric to the assumed interhemispheric mid-sagittal
plane by flipping the normalized 3-D bloodstream image (step S226).
Then, CPU 12 calculates similarity index between the original and
the flipped normalized 3-D bloodstream images (step S227). In this
embodiment, Stochastic Sign Change (SSC) is calculated as
similarity index.
[0077] Concept of calculating SSC is as follows:
[0078] Consider two similar but not identical images I.sub.1(x, y)
and I.sub.2(x, y), where I(x, y) is the pixel count and x, y=1, 2,
. . . n are the coordinates of the digitized images. Let S(x,
y)=I.sub.1(x, y)-I.sub.2(x, y) be the subtraction image. If
I.sub.1(x, y) and I.sub.2(x, y) contain additive noise which can be
assumed to have a zero mean with a symmetric density function, each
pixel value of S(x, y) is not zero but shows random fluctuations
around zero, either positive or negative values with equal
probability. If there is a dissimilar part of the images between
I.sub.1(x, y) and I.sub.2(x, y), the pixel values of S(x, y) in
that part will no longer exhibit random fluctuations and will show
groupings of all positive or negative values. Let SSC represent the
number of sign changes in a sequence of the S(x, y), scanned
line-by-line or column-by-column. Accordingly, SSC shows a lager
number of sign changes when I.sub.1(x, y) and I.sub.2(x, y) are
similar and a lower value when I.sub.1(x, y) and I.sub.2(x, y) are
dissimilar. Therefore, the SSC criterion can be defined as a
similarity criterion between two images. This concept can be
applied on judging the similarity between two three-dimensional
images.
[0079] SSC is calculated by summation of SSC.sub.x, SSC.sub.y and
SSC.sub.z in this embodiment. Referring to FIG. 14, planes
ZX.sub.1, ZX.sub.2, . . . ZX.sub.N and planes YX.sub.1, YX.sub.2, .
. . YX.sub.N are assumed for calculating SSC x. For each plane, SSC
is calculated by scanning the plane. SSC.sub.x is calculated by
summation of SSC of all planes ZX.sub.1, ZX.sub.2, . . . ZX.sub.N
and YX.sub.1, YX.sub.2, . . . YX.sub.N. CPU 12 also calculates
SSC.sub.y by summation of SSC of planes ZY.sub.1, ZY.sub.2, . . .
ZY.sub.N and planes YX.sub.1, YX.sub.2, . . . YX.sub.N and
SSC.sub.z by summation of SSC of planes ZY.sub.1, ZY.sub.2, . . .
ZY.sub.N and planes ZX.sub.1, ZX.sub.2, . . . ZX.sub.N. Then, CPU
12 obtains SSC(.DELTA.x, .DELTA..phi.z, .DELTA..phi.y) with regard
to the assumed interhemispheric mid-sagittal plane as summation of
SSC.sub.x, SSC.sub.y and SSC.sub.z and stores SSC(.DELTA.x,
.DELTA..phi.z, .DELTA..phi.y) on the memory 20.
[0080] CPU 12 changes .DELTA.x from -I to I at 1 voxel step to move
the assumed interhemispheric mid-sagittal plane along X axis and
changes .DELTA..phi.z and .DELTA..phi.y from -.phi.I to .phi.I to
rotate the assumed interhemispheric mid-sagittal plane. CPU 12
calculates SSC(.DELTA.x, .DELTA..phi.z, .DELTA..phi.y) for all
assumed interhemispheric mid-sagittal planes which are determined
by all combinations of .DELTA.x, .DELTA..phi.z and .DELTA..phi.y
(step S221, S223 and S224).
[0081] After calculating SSC(.DELTA.x, .DELTA..phi.z,
.DELTA..phi.y) of the all assumed interhemispheric mid-sagittal
planes, CPU 12 identifies the interhemispheric mid-sagittal plane
which has the largest SSC(.DELTA.x, .DELTA..phi.z, .DELTA..phi.y)
among the all assumed interhemispheric mid-sagittal planes (step
S229). Identifying the interhemispheric mid-sagittal plane is
disclosed in Minoshima et al., "An Automated Method for Rotational
Correction and Centering of Three-Dimensional Functional Brain
Images" J Nucl Med 1992; 33: 1579-1585, which is expressly
incorporated by reference herein.
3.2.2 Identifying AC-PC Line and the Center Thereof
[0082] After identifying the interhemispheric mid-sagittal plane,
CPU 12 identifies line which passes through the anterior and the
posterior commissures of the brain (herein after referred to "AC-PC
line") and the center thereof (step S23 of FIG. 10). In this
embodiment, four landmarks, occipital pole point (OP), the
subthalamic point (TH), the inferior aspect of the anterior corps
callosum (CC) and the frontal pole point (FP) of the brain are
decided and then AC-PC line is identified as straight line
connecting the 4 landmarks.
[0083] Detailed flowchart for identifying AC-PC line and center
thereof is shown in FIG. 15. CPU 12 reads out sectional bloodstream
image at the interhemispheric mid-sagittal plane from the hard-disk
14. CPU 12 further reads out sectional bloodstream images of planes
which are parallel to and near the interhemispheric mid-sagittal
plane (step S231). FIG. 16A shows an example of the sectional
bloodstream image at the interhemispheric mid-sagittal plane.
[0084] CPU 12 detects outer border of brain and border between
white matter and gray matter of brain for each normalized 3-D
bloodstream image (step S232). The detection can be carried out by
detecting the border between the area of bloodstream existing and
the area of bloodstream non-existing. FIG. 16B shows detected
border image of the interhemispheric mid-sagittal plane for
example.
[0085] Then, CPU 12 identifies the occipital pole point (OP) as the
most posterior point of the border image of the interhemispheric
mid-sagittal plane (see FIG. 16B). CPU 12 also identifies the
occipital pole points (OPs) for the other border images of the
other sectional bloodstream images. CPU 12 calculates mean of y
coordinate values and mean of z coordinate values of all the
identified occipital pole points (OPs). CPU 12 decides the position
of the occipital pole point (OP) based on the mean of y coordinates
values and mean of z coordinate values on the interhemispheric
mid-sagittal plane (step S233).
[0086] Next, CPU 12 finds out U shaped border area as shown in FIG.
16C. The inferior aspect of the anterior corps callosum (CC) is
decided by detecting contact point of U shaped border and tangent
line from the occipital pole point (OP) to the U shaped border
based on the border image. CPU 12 decides the inferior aspect of
the anterior corps callosum (CC) for each border image of the
sectional bloodstream image. CPU 12 calculates mean of y coordinate
values and mean of z coordinate values of all the found inferior
aspect of the anterior corps callosum (CC). CPU 12 decides the
position of the inferior aspect of the anterior corps callosum (CC)
based on the mean of y coordinate values and mean of z coordinate
values on the interhemispheric mid-sagittal plane (step S234).
[0087] CPU 12 identifies the frontal pole point (FP) as the most
anterior point of the border image of the interhemispheric
mid-sagittal plane (see FIG. 16B). CPU 12 also identifies the
frontal pole points (FPs) based on the other border images of the
other sectional bloodstream images. CPU 12 calculates mean of y
coordinate values and mean of z coordinate values of all the
identified frontal pole points (FPs). CPU 12 decides the position
of the frontal pole point (FP) based on the mean of y coordinate
values and mean of z coordinate values on the interhemispheric
mid-sagittal plane (step S235).
[0088] CPU 12 decides the subthalamic point (TH) (step S236). To
decide the subthalamic point (TH), CPU 12 finds out the thalamic
center at first. The thalamic center can be estimated as the point
having local maximum bloodstream associated value and near the
AC-PC line. In this embodiment, CPU 12 finds out a point having the
local maximum bloodstream associated value within the circle of
radius r on the sectional bloodstream image and identifies the
point as the thalamic center, where center of the circle is a
center point of line connecting the occipital pole point (OP) and
the frontal pole point (FP) and the radius r is tenth of the length
between OP and FP.
[0089] After deciding the thalamic center, CPU 12 plots an
imaginary circle having predetermined radius r on the sectional
bloodstream image. As shown in FIG. 17A, CPU 12 makes imaginary
point P1 where the imaginary circle and tangent line form OP to the
imaginary circle contact and imaginary point P2 which is
diametrically opposite to P1 on the imaginary circle. CPU 12 stores
bloodstream associated value on each imaginary point P2 of the
imaginary circle on the memory 20 when the radius r of the
imaginary circle is varied from the length of 1 pixel to 12 pixels
by 0.5 pixel step. FIG. 17B shows a graph plotting the bloodstream
associated value on the imaginary point P2 at each radius r. In
this graph, horizontal axis denotes the radius r and r=0 is
positioned at the center. To the left and right sides from the
center, r is increased. The graph shows bloodstream associated
value becomes maximum when the radius r is 0. Increasing r makes
bloodstream associated value on the imaginary point P2 reduced
toward minimum bloodstream associated value which corresponds to
lateral ventricular (LV).
[0090] CPU 12 decides radius r which corresponds border of thalamus
at which the bloodstream associated value on the imaginary point P2
shows 70% of the maximum bloodstream associated value where the
minimum bloodstream associated value is set as 0% (see FIG. 17).
Then, CPU 12 identifies the imaginary point P1 on the imaginary
circle having the decided radius r as the subthalamic point (TH).
CPU 12 also decides the subthalamic point (TH) based on the other
sectional bloodstream images. CPU 12 calculates mean of y
coordinate values and mean of z coordinate values of all the
decided subthalamic points (THs). CPU 12 decides the position of
the subthalamic point (TH) based on the mean of y coordinate values
and mean of z coordinate values on the interhemispheric
mid-sagittal plane (see FIG. 16B).
[0091] CPU 12 generates regression line connecting through the
occipital pole point (OP), the subthalamic point (TH), the inferior
aspect of the anterior corps callosum (CC) and the frontal pole
point (FP) on the interhemispheric mid-sagittal plane. CPU 12
identifies the generated regression line as the AC-PC line a (step
S237). Then, center point .beta. of the AC-PC line a is decided as
a point which possesses half length of the AC-PC line as shown in
FIG. 16D (step S238). Identifying the AC-PC line and the center
thereof is disclosed in Minoshima et al., "An Automated Detection
of the Intercommissural Line for Stereotactic Localization of
Functional Brain Images" J Nucl Med 1993; 34: 322-329, which is
expressly incorporated by reference herein.
3.2.3 Positioning Subject's Image to Normal Brain
[0092] Once the AC-PC line and the center thereof are identified,
CPU 12 positions the normalized 3-D bloodstream image of each
subject to the standard brain image data 28 recorded in the
hard-disk 14 by superposing the AC-PC lines and the centers of both
images (step S24 of FIG. 10). Direction of subject's normalized 3-D
bloodstream image is aligned to that of the standard brain image by
matching the AC-PC line of the subject's normalized 3-D bloodstream
image to that of the standard brain image. Position of subject's
normalized 3-D bloodstream image is aligned to that of the standard
brain image in the anteroposterior direction by matching the center
of the AC-PC line to that of the standard brain image.
[0093] In this embodiment, .sup.18F-FDG PET image is used as the
standard brain image. The standard image is made by transforming
the images of a number of normal subjects in accordance with the
standard brain shape and averaging them. The standard brain image
of .sup.18F-FDG PET is well-used and easily obtainable.
3.2.4 Linearly Transforming Subject's Image
[0094] After positioning the subject's normalized 3-D bloodstream
image to the normal brain, CPU 12 linearly transforms the subject's
normalized 3-D bloodstream image in accordance with the standard
brain image by the following steps.
[0095] First, CPU 12 provides Y-axis which is the AC-PC line of the
subject's normalized 3-D bloodstream image, Z-axis which is the
line on the interhemispheric mid-sagittal plane which passes
through the center of the AC-PC line and is perpendicular to the
AC-PC line and X-axis which is the normal line of the
interhemispheric mid-sagittal plane which passes through the center
of the AC-PC line. The center of the AC-PC line is, therefore,
provided as the original point of the coordinate system. Then, CPU
12 obtains Y coordinate positions of the most anterior and the
posterior points, Z coordinate positions of the most upper and the
lowest points and X coordinate positions of the right most and the
left most points of the subject's normalized 3-D bloodstream image.
CPU 12 also obtains the Y coordinate positions, the z coordinate
positions and the X coordinate positions of the standard brain
image which may be previously obtained and stored.
[0096] CPU 12 transforms the subject's normalized 3-D bloodstream
image so that the outer border of the subject's bloodstream image
is adjusted to the outer border of the standard brain image by
using the obtained X, Y and Z coordinate positions of the subject's
normalized 3-D bloodstream image and shoes of the standard brain
image (step S25 of FIG. 10).
[0097] Referring to FIG. 18, for example, the subject's normalized
3-D bloodstream image (shown by solid line) is superposed on the
standard brain image (shown by chained line). CPU 12 finds the most
upper point T of the outer border of the subject's normalized 3-D
bloodstream image and obtains Z coordinate value Lz of the point T.
CPU 12 also finds the most upper point T' of the outer border of
the standard brain image and obtains Z coordinate value L'z of the
point T'. Then, CPU 12 transforms Z coordinate values of the
subject's normalized 3-D bloodstream image by the following
equation:
Z'=Z(Lz/L'z)
where Z' is the transformed Z coordinate value of the subject's
normalized 3-D bloodstream image, Z is the original Z coordinate
value of the subject's normalized 3-D bloodstream image, Lz is the
Z coordinate value of the point T and L'z is the Z coordinate value
of the point T'.
[0098] By the transformation, the subject's normalized 3-D
bloodstream image is adjusted to the standard brain image with
respect to upper direction.
[0099] CPU 12 also transforms the subject's normalized 3-D
bloodstream image with respect to lower, light, left, anterior and
posterior direction by the same way as that of the upper
direction.
3.2.5 Non-Linearly Transforming Subject's Image
[0100] Although the subject's normalized 3-D bloodstream image is
aligned in accordance with the standard brain image by the liner
transforming, some misalignments between the both images still
remain. CPU 12 partially transforms the subject's normalized 3-D
bloodstream image based on profile curve of the bloodstream
associated value of the subject's normalized 3-D bloodstream image
and profile curve of the glucose metabolism of the standard brain
image (step S26).
[0101] Referring to FIG. 19A, plural landmarks C1, C2 . . . Cn on
white matter are predetermined in the standard brain image. Plural
landmarks S1, S2 . . . Sn on brain surface are also predetermined
corresponding to the landmarks C1, C2 . . . Cn in the standard
brain image. In FIG. 19A, the landmarks S1, S2 and S3 of brain
surface are provided corresponding to the landmarks C1 of white
matter. CPU 12 generates the profile curve of the bloodstream
associated values by obtaining the bloodstream associated values on
the subject's normalized 3-D bloodstream image along a line L1
connecting the landmarks from C1 to S1 (see solid curve of FIG.
19B). CPU 12 obtains the profile curve of glucose metabolism on the
standard brain image along the line L1 (see chained curve of FIG.
19B). In this embodiment, the profile curve of glucose metabolism
on the standard brain image is prerecorded in the hard-disk 14.
[0102] CPU 12 partially transforms the subject's normalized 3-D
bloodstream image so that the profile curve of the bloodstream
associated values of the subject's normalized 3-D bloodstream image
accords to the profile curve of the glucose metabolism values at
each corresponding point, because the profile curve of the glucose
metabolism values is well matched with that of the bloodstream
associated values. The partial transformation (non-liner
transformation) is carried out along the direction of line L1 and
the landmark C1 is fixed. With regard to the line L2 between the
landmarks C1 and S2 and the line L3 between the landmarks C1 and
S3, CPU 12 also transforms the subject's normalized 3-D bloodstream
image by same way. In area between the lines L1 and L2 (L2 and L3),
CPU 12 transforms these areas based on the transforming rates on L1
and L2 (L2 and L3). The non-liner transformation is carried out
with regard to each line L connecting each landmark C of white
matter and corresponding landmark S of brain surface. Although in
the above description, non-linear transformation is described on
two-dimensional model, the non-linear transformation is carried out
in three-dimensional space in this embodiment.
[0103] The liner and non-liner transforming is disclosed in
Minoshima et. al. "An Anatomic Standardization: Liner Scaling and
Nonliner Warping of Functional Brain Images" J Nucl Med 1994; 35:
1528-1537, which is expressly incorporated by reference herein.
3.2.6 Generating Brain Surface Bloodstream Image
[0104] After anatomically transforming the subject's transformed
3-D bloodstream image, CPU 12 generates the brain surface
bloodstream image (step S27 of FIG. 11). FIG. 20 shows detailed
flowchart for generating the brain surface bloodstream image.
[0105] CPU 12 identifies each brain surface voxel positioned at the
most outer surface of the subject's normalized 3-D bloodstream
image and assigns identification number for each identified surface
voxel (step S271). In this embodiment, approximate 20,000 surface
voxels are identified.
[0106] CPU 12 carries out the following steps for each identified
surface voxel:
[0107] CPU 12 obtains bloodstream associated values of several
number ("6" in this embodiment) of voxels from the brain surface to
predetermined depth perpendicular to the brain surface in the
subject's transformed 3-D bloodstream image (step S273). Then, CPU
12 selects the maximum bloodstream associated value among the
several numbers of voxels as representative bloodstream associated
value and the selected maximum bloodstream associated value is
assigned to the brain surface voxel (step S274).
[0108] CPU 12 carries out such process for each brain surface
voxel. Then, brain surface bloodstream image in which the
representative bloodstream associated value is indicated at each
surface voxel is obtained.
[0109] CPU 12 stores the brain bloodstream images generated in step
S26 and the brain surface bloodstream images generated in step S27
on the hard-disk 14 (step S28 of FIG. 11).
[0110] When next subject's unprocessed bloodstream image exists,
CPU 12 reads out the next subject's unprocessed bloodstream image
from the hard-disk 14 (step S30) and carries out the process
described above (following steps of step S22 of FIG. 10). When all
subject's bloodstream images are processed (step S29), CPU 12
finishes the anatomical standardization (step S2 of FIG. 6).
3.3 Generating Alternative Normal Brain Database
[0111] After generating anatomically standardized subject's brain
bloodstream image and brain surface bloodstream image, CPU 12
generates alternative normal brain database (step S3 of FIG. 6).
When all subjects are normal healthy, the alternative normal brain
database can be simply obtained by calculating mean value and
standard deviation of each point of the brain bloodstream images
and brain surface bloodstream images of anatomically standardized.
However, the subjects contain patient(s) in this embodiment.
Therefore, the alternative normal brain database is generated in
this embodiment after rejecting bloodstream associated values
regarded as disease p art.
[0112] FIGS. 21 and 22 show detailed flowchart for generating the
alternative normal brain database. CPU 12 reads out the normalized
bloodstream associated values of target voxel of all subject's
standardized brain bloodstream images (step S34). Then, CPU 12
sorts the bloodstream associated values in descending order (step
S35). FIG. 23 shows sorted bloodstream associated values of the
target voxel. In FIG. 23, the bloodstream associated values of
subjects at target voxel (positioned at x=11, y=25 and z=135) are
shown in the descending order. Subject ID means identification of
subject which assigned to each subject uniquely.
[0113] CPU 12 rejects bloodstream associated values that are ranked
in the top 10% and in the bottom 40% (step S36). The percentage of
the rejecting may be selected arbitrarily. When the number of the
subjects are 20 for example, bloodstream associated values of the
highest 2 subjects and of the lowest 8 subjects are rejected. In
this embodiment, bottom values are rejected, because a part having
reduced bloodstream associated value is regarded as corresponding
to disease part such as Alzheimer's disease. Higher values are also
rejected, because it sometimes occurs that even the bloodstream
associated value of the part except the disease part vary widely
from those of the normal healthy subjects due to the influence of
the normalization (step S13 of FIG. 7). Because the lower values
are regarded as corresponding to the disease part, rejected
percentages of the lower values are higher than that of the higher
values in this embodiment.
[0114] Then, CPU 12 calculates and stores mean value and standard
deviation of remaining bloodstream associated values after the
rejection (step S37). As described above, mean value and standard
deviation of the bloodstream associated values at target voxel (11,
25, 135) are calculated and stored on the hard-disk 14 (see FIG.
24).
[0115] CPU 12 calculates mean value and standard deviation of
bloodstream associated values for each voxel as target voxel (steps
S31, S32, S33 and S38). Then, the alternative normal brain database
of brain bloodstream image is obtained as shown in FIG. 24.
[0116] Next, CPU 12 calculates mean value and standard deviation of
bloodstream associated values with regard to the patient's brain
surface bloodstream images. CPU 12 reads out the bloodstream
associated values of target surface voxel of all the subject's
standardized brain bloodstream images (step S40). Then, CPU 12
sorts the bloodstream associated values in descending order (step
S41).
[0117] CPU 12 rejects bloodstream associated values that are ranked
in the top 10% and in the bottom 40% (step S42). When the number of
the subjects are 20 for example, bloodstream associated values of
the highest 2 subjects and of the lowest 8 subjects are
rejected.
[0118] Then, CPU 12 calculates and stores mean value and standard
deviation of remaining bloodstream associated values after the
rejection (step S43). The alternative normal brain database of
brain surface bloodstream image is obtained as shown in FIG.
25.
4 Other Embodiments
[0119] Identifying interhemispheric mid-sagittal plane and PC-AC
line and positioning AC-PC line shown as steps S22, S23 and S24 of
FIG. 10 may be substituted by aligning method using mutual
information in which the subject's bloodstream image is moved
against the standard brain image in dribs and mutual information
between the both images is used to find the best aligning position.
The aligning method using mutual information is disclosed in F.
Maes et al., "Multimodality Image Registration by Maximization of
Mutual Information," IEEE Transactions on Medical Imaging, (USA),
1997, 16, 2, p 187-198, which is expressly incorporated by
reference herein. The aligning method using mutual information can
be achieved by the stereo program contained in 3D-SSP program
(provided by Satoshi Minoshima, Professor of University of
Washington).
[0120] Although the bloodstream associated values regarded as
outlier due to the normalization are rejected in addition to the
bloodstream associated values regarded as brain disease part in the
above mentioned embodiment. However, only one of the bloodstream
associated values regarded as outlier due to the normalization or
the bloodstream associated values regarded as brain disease part
may be rejected.
[0121] It is suitable to assume the bloodstream associated values
indicating disease part and reject the predetermined percentage of
the lower bloodstream associated values as described in the above
mentioned embodiment when the number of subjects is large and the
predetermined percentage can be accurately predicted, because the
rejection can be carried out by simple calculation.
[0122] Instead of the above mentioned rejection method, following
statistical rejection methods may be used. For example, THOMPSON's
method (William R. Thompson, The annals of Mathematical Statistics,
Vol 6, No. 4 (December 1935), pp 214-219), SMIRNOV-GRUBBS's method
(Frank E. Grubbs, The annals of Mathematical Statistics, Vol 21,
No. 1 (March, 1950), pp. 27-58) may be used.
[0123] When using THOMPSON's method, steps S35 and S36 of FIG. 21
are substituted for steps shown in FIG. 26.
[0124] Referring to FIG. 26, CPU 12 sets a bloodstream associated
value Ip (x, y, z) of a subject as target subject's bloodstream
associated value (step S352). CPU 12 calculates mean value
I.sub.mean (x, y, z) and standard deviation SD (x, y, z) of
bloodstream associated values of all subjects (step S353). Then,
CPU 12 calculates index To according to following equation (step
S354):
T.sub.0=(Ip(x,y,z)-I.sub.mean(x,y,z))/SD(x,y,z)
[0125] CPU 12 compares the index T.sub.0 and limit Ta (step S355).
The limit Ta is decided by combination of risk rate .alpha. and the
number of subjects M. In this embodiment, CPU 12 obtains the limit
Ta according to a table (per-recorded on the hard-disk 14) as shown
in FIG. 27. The risk rate .alpha. is a factor indicating rate of
rejection and more bloodstream associated values are rejected when
the risk rate .alpha. grows higher. When the number M of subject is
20 and the risk rate .alpha. is 5%, the limit Ta is decided as 1.93
from the table of FIG. 27.
[0126] CPU 12 rejects the target subject's bloodstream associated
value Ip (x, y, z) when the index T.sub.0 is larger than the limit
Ta (step S35) and does not reject the target subject's bloodstream
associated value Ip (x, y, z) when the index T.sub.0 is not larger
than the limit Ta.
[0127] CPU 12 repeatedly carries out the above mentioned process
for each subject's bloodstream associated value as the target
bloodstream associated value (steps S351 and S357).
[0128] In the embodiment shown in FIG. 26, the same risk rate
.alpha. is used for both the upper side and the lower side outlier.
However, the risk rate a of one side on which the disease affects
the bloodstream (for example, lower bloodstream associated value
side) may be higher than that of the other side on which the
disease does not affect the bloodstream (for example, higher
bloodstream associated value side). Instead, the rejection process
may be carried out only for the one side in which the disease
affects the bloodstream.
[0129] As shown in the table of FIG. 27, the number of subjects
should be more than 3 for rejecting process. In order to secure the
degrees of freedom, it is necessary to have predetermined number of
subjects for the statistical rejecting process such as Thompson's
method.
[0130] Although in the above mentioned embodiments, brain
bloodstream image obtained by SPECT and PET etc. is used, other
functional images may be used.
[0131] Although in the above mentioned embodiments, normalization
of status values (bloodstream associated values) is carried out
before the spatial transformation, the normalization may be carried
out after the spatial transformation or the normalization and the
spatial transformation may be carried out simultaneously.
[0132] While the embodiments of the present invention disclosed
herein are presently considered to be preferred embodiments,
various changes and modifications can be made without departing
from the spirit and scope of the present invention. The scope of
the invention is indicated in the appended claims, and all changes
that come within the meaning and range of equivalents are intended
to be embraced therein.
* * * * *