METHOD FOR DISTINGUISHING BETWEEN GRAY MATTER AND WHITE MATTER AND CT SYSTEM FOR CARRYING OUT THE METHOD

• US 20100166281A1
• Filed: 12/18/2009
• Published: 07/01/2010
• Est. Priority Date: 12/22/2008
• Status: Active Grant

• Current Assignee: Siemens Healthcare GMBH (Siemens AG)
• Original Assignee: Siemens AG
• Inventors: Schleu, Jochen, Klotz, Ernst, Soza, Grzegorz, Bürger, Corinna
• Granted Patent: US 8,467,587 B2
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/4014 arranged in multiple source-detector units
• CPC Class Codes: A61B 6/4266 characterised by using a plurality of detector units A61B6/4014 takes precedence
• CPC Class Codes: A61B 6/481 involving the use of contrast agents
• CPC Class Codes: A61B 6/504 for diagnosis of blood vessels, e.g. by angiography
• CPC Class Codes: A61B 6/507 for determination of haemodynamic parameters, e.g. perfusion CT
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/174 involving the use of two or more images

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

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.

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

3. 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.

4. 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.

5. The method as claimed in claim 3, wherein the following formula is used to calculate the average image (A):

6. 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.

7. The method as claimed in claim 3, wherein the following formula is used to calculate the baseline image (B):

8. 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.

9. 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.

10. 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.

11. The method as claimed in claim 3, wherein the following formula is used to generate the high-contrast image (K):

12. 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.

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

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

15. 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.

16. 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.

17. 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.

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

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

20. 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.

21. 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.

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

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

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

25. 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.

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

27. The method as claimed in claim 26, wherein the exclusion operator (A) corresponds to the following formula:

A=RG_ig+RG*_gg+R_G with R_G=G−

└

RG_ig+RG*_gg+RG_gw┘

,

wherein G corresponds to the entire cerebral tissue and R_G corresponds to all regions of the brain that have not previously been acquired.

28. 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.

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

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

31. 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.