# US 2010/0166281 A1 · Method for distinguishing between gray matter and white matter and ct system for carrying out the method

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- Number: US 2010/0166281 A1
- Document ID: US20100166281A1
- Status: Published application
- Kind: A1
- Application number: 12/641,452
- Filed: 2009-12-18
- Published: 2010-07-01
- Earliest claimed: 2008-12-22
- Assignees: Siemens Aktiengesellschaft
- Inventors: Corinna Bürger; Ernst Klotz; Jochen Schleu; Grzegorz Soza
- Web page: https://patentgrep.com/patent/US20100166281A1

## CPC

- A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography}
- A61B 6/032: {Transmission computed tomography [CT]}
- A61B 6/481: {involving the use of contrast agents}
- A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT}
- G06T 7/11: Region-based segmentation
- G06T 7/136: involving thresholding
- G06T 7/174: involving the use of two or more images
- A61B 6/4014: {arranged in multiple source-detector units}
- A61B 6/4266: {characterised by using a plurality of detector units (A61B6/4014 takes precedence)}
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/30016: Brain
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A method for distinguishing between gray matter and white matter starting from a time-dependent computed tomography image data record from a perfusion CT examination is disclosed. In at least one embodiment, a plurality of time-independent images are calculated from the time-dependent image data record, a plurality of threshold histogram analyses are performed in order to determine regions of the brain which can be assigned to one or more types of cerebral matter, and subsequently the region of gray matter is determined from the information obtained in respect of type and region of the cerebral matter using at least one logical combination and at least one exclusion method. A control and computational unit is also disclosed with a storage medium in which a computer program or program module is stored, which executes the described method during operation.

## Claims

### Claim 1

A method for determining a region of gray matter in a brain starting from a time-dependent computed tomography image data record from a perfusion CT examination, the method comprising:
calculating a plurality of time-independent images from the time-dependent computed tomography image data record;
performing a plurality of threshold histogram analyses in order to determine regions of the brain which are assignable to one or more types of cerebral matter; and
subsequently determining a region of gray matter from the information obtained with respect to the determined regions and types of the cerebral matter using at least one logical combination and at least one exclusion method.

### Claim 2 (depends on 1)

The method as claimed in claim 1, wherein the time-independent images are registered to the time-dependent computed tomography image data record.

### Claim 3 (depends on 1)

The method as claimed in claim 1, comprising:
defining a work region in the time-dependent computed tomography image data record which only comprises healthy gray matter, healthy white matter and ischemic gray matter, with all further steps being performed in the defined work region,
calculating the following three-dimensional image data records from the time-dependent computed tomography image data record:
an average image with and without a contrast agent being present,
a maximum value image over time, and
a baseline image without a contrast agent being present,
determining a difference image from the average image and the maximum value image,
determining a normalized difference image from the image data of the difference image,
generating a high-contrast image with increased contrast between white matter and gray matter from the average image, the maximum value image and the normalized difference image,
determining a first grayscale value range, which only comprises gray matter, and a second grayscale value range, which only comprises white matter, by threshold histogram analysis of the high-contrast image, wherein only the image data of the undamaged hemisphere is considered,
determining a third grayscale value range which comprises both healthy white matter and ischemic gray matter by threshold histogram analysis of the normalized difference image,
determining a fourth grayscale value range which comprises both healthy gray matter and ischemic gray matter by threshold histogram analysis of the baseline image,
generating a fifth grayscale value range which lies in the damaged hemisphere and only comprises ischemic gray matter by a logical combination of the third grayscale value range and the fourth grayscale value range,
determining seed points for respectively the first grayscale value range, the second grayscale value range and the fifth grayscale value range,
performing a region growing method respectively starting at the previously determined seed points and generating a first image region, a second image region, and a fifth image region,
generating an expanded first image region by adding cerebral regions which constitute anatomically assured gray matter to the first image region, and
determining a resultant image region which comprises both healthy gray matter and ischemic gray matter and all regions of the brain which have not yet been acquired by way of a logical combination of the changed first image region, the second image region and the fifth image region.

### Claim 4 (depends on 3)

The method as claimed in claim 3, wherein a region which, starting from the pia mater, reaches one centimeter into the cerebral matter is used as the anatomically assured gray matter.

### Claim 5 (depends on 3)

The method as claimed in claim 3, wherein the following formula is used to calculate the average image (A):
A
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(
x
,
y
,
z
)
=
1
N

∑
t
=
t
1
t
=
t
N

I
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(
x
,
y
,
z
,
t
)
,
wherein t is the time of the recording, N is the total number of records and I is the intensity at the time of the respective recording.

### Claim 6 (depends on 3)

The method as claimed in claim 3, wherein the following formula is used to calculate the maximum value image (T):
T(x,y,z)=maxI(x,y,z,t),
wherein t is the time of a recording and I is the intensity at the time of the respective recording.

### Claim 7 (depends on 3)

The method as claimed in claim 3, wherein the following formula is used to calculate the baseline image (B):
B

(
x
,
y
,
z
)
=
1
M
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∑
t
=
t
1
t
=
t
M
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I
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(
x
,
y
,
z
,
t
)
,
wherein t is the time of the recording, M is the number of records without a contrast agent and I is the intensity at the time of the respective recording.

### Claim 8 (depends on 3)

The method as claimed in claim 3, wherein the image data of the average image is subtracted from the image data of the maximum value image in order to determine the difference image.

### Claim 9 (depends on 3)

The method as claimed in claim 3, wherein a lower bound and an upper bound are determined in the difference image, as is an average maximum grayscale value.

### Claim 10 (depends on 9)

The method as claimed in claim 9, wherein the grayscale values are divided by the previously determined average maximum grayscale value in order to normalize the difference image.

### Claim 11 (depends on 3)

The method as claimed in claim 3, wherein the following formula is used to generate the high-contrast image (K):
K
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(
x
,
y
,
z
)
=
{
D
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(
x
,
y
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≤
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m
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i

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n
;
A
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x
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,
z
)
D
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(
x
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)
≥
D
m
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ax
;
T
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(
x
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,
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otherwise
;
[
T
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x
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·
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x
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]
+
[
A
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·
(
1
-
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(
x
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]
.

### Claim 12 (depends on 3)

The method as claimed in claim 3, wherein the threshold histogram analysis in order to determine the first and the second grayscale value range is performed under the assumption that the histogram corresponds to a unimodal Gaussian distribution.

### Claim 13 (depends on 12)

The method as claimed in claim 12, wherein parameters of the Gaussian distribution are determined.

### Claim 14 (depends on 13)

The method as claimed in claim 13, wherein a maximum likelihood method is applied in order to determine the parameters.

### Claim 15 (depends on 13)

The method as claimed in claim 13, wherein an upper and a lower threshold are determined starting from the parameters, and wherein the image data lies above the upper threshold within the first grayscale value range and the image data lies below the lower threshold within the second grayscale value range.

### Claim 16 (depends on 3)

The method as claimed in claim 3, wherein only one threshold which corresponds to an average grayscale value of the histogram is used in the threshold histogram analysis in order to determine the third grayscale value range, and wherein the image data lies below the threshold within the third grayscale value range.

### Claim 17 (depends on 3)

The method as claimed in claim 3, wherein the threshold histogram analysis, in order to determine the fourth grayscale value range, is performed under the assumption that the histogram corresponds to a unimodal Gaussian distribution.

### Claim 18 (depends on 17)

The method as claimed in claim 17, wherein parameters of the Gaussian distribution are determined.

### Claim 19 (depends on 18)

The method as claimed in claim 18, wherein a maximum likelihood method is applied in order to determine the parameters.

### Claim 20 (depends on 19)

The method as claimed in claim 19, wherein an upper threshold is determined starting from these parameters, and wherein the image data of the fourth image region lies above the threshold.

### Claim 21 (depends on 3)

The method as claimed in claim 3, wherein a logical AND operator is utilized in the logical combination for extracting the fifth grayscale value range.

### Claim 22 (depends on 3)

The method as claimed in claim 3, wherein two different termination criteria are used in the region growing method.

### Claim 23 (depends on 22)

The method as claimed in claim 22, wherein a static termination criterion is used.

### Claim 24 (depends on 22)

The method as claimed in claim 22, wherein a dynamic termination criterion is used.

### Claim 25 (depends on 24)

The method as claimed in claim 24, wherein the dynamic termination criterion is used in the region growing method in order to generate the fifth image region.

### Claim 26 (depends on 3)

The method as claimed in claim 3, wherein the resultant image region is determined using an exclusion operator.

### Claim 27 (depends on 26)

The method as claimed in claim 26, wherein the exclusion operator (A) corresponds to the following formula:
A=RGig+RG*gg+RG with RG=G−└RGig+RG*gg+RGgw┘,
wherein G corresponds to the entire cerebral tissue and RG corresponds to all regions of the brain that have not previously been acquired.

### Claim 28 (depends on 1)

A control and computational unit comprising:
a storage medium in which a computer program or program module is stored, the control and computational unit being adapted to execute the method as claimed in claim 1 during operation.

### Claim 29 (depends on 13)

The method as claimed in claim 13, wherein the parameters of the Gaussian distribution include the maximum value and the standard deviation.

### Claim 30 (depends on 18)

The method as claimed in claim 18, wherein the parameters of the Gaussian distribution include the maximum value and the standard deviation.

### Claim 31 (depends on 1)

A computer readable medium including program segments for, when executed on a computer device, causing the computer device to implement the method of claim 1.