Technologies for diagnosing neurological or psychiatric illnesses
A technology which enables identifying, via a computer, a vessel in a third image. The third image is obtained from a subtraction of a second image from a first image. The second image and the first image are aligned on an imaging space. The first image is post-contrast. The second image is pre-contrast. The technology enables determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image. The technology enables obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value. The technology enables applying, via the computer, a filter onto the fourth image. The technology enables generating, via the computer, a filter mask based on the fourth image.
1. A method comprising:
identifying, via a computer, a vessel in a third image, wherein the third image is obtained from a subtraction of a second image from a first image, wherein the second image and the first image are aligned within an imaging space, wherein the first image is post-contrast, wherein the second image is pre-contrast;
determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image based on a highest voxel intensity in the segment of the vessel such that the voxel intensity mean value indicates at least one of a content of a blood or a flow of the blood in the segment of the vessel;
obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value such that a percentage of a volume of the blood shown in the segment of the fourth image is determinable;
applying, via the computer, a filter onto the fourth image; and
generating, via the computer, a filter mask based on the fourth image.
2. The method of claim 1 , further comprising:
performing, via the computer, a vessel segmentation process on at least one of the first image or the second image before the identifying.
3. The method of claim 2 , wherein the performing is automatically triggered.
4. The method of claim 1 , wherein the vessel comprises a diameter of about one centimeter or less.
5. The method of claim 1 , wherein the identifying is based on a vesselness filter and a pre-defined region of interest, wherein the vesselness filter filters based on a set of eigenvalues of a Hessian matrix of the third image, wherein the third image is modified such that the region of interest is positioned in a predefined area.
6. The method of claim 1 , wherein the voxel intensity mean value is based on a highest voxel intensity range in the segment, wherein the range comprises a top 40% of voxel intensities.
7. The method of claim 6 , wherein the range comprises a top 33% of voxel intensities.
8. The method of claim 6 , wherein the range comprises a top 25% of voxel intensities.
9. The method of claim 1 , wherein the third image is a cerebral blood volume map, wherein the filter is based on at least one of a performance of an expectation-maximization segmentation, or a fitting of a bimodal Gaussian curve to a histogram of data in accordance with the third image.
10. The method of claim 1 , wherein the filter mask is a binary mask, and further comprising:
applying, via the computer, the binary mask to the third image; and
mapping, via the computer, based on the applying, the third image according to a change in a transverse relaxation time induced via an input of a contrast agent.
11. A system comprising:
a hardware processor;
a memory coupled to the hardware processor, wherein the memory stores a set of instructions to execute via the hardware processor, wherein the instructions instruct the hardware processor to perform a method comprising:
identifying, via a computer, a vessel in a third image, wherein the third image is obtained from a subtraction of a second image from a first image, wherein the second image and the first image are aligned within an imaging space, wherein the first image is post-contrast, wherein the second image is pre-contrast;
determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image based on a highest voxel intensity in the segment of the vessel such that the voxel intensity mean value indicates at least one of a content of a blood or a flow of the blood in the segment of the vessel;
obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value such that a percentage of a volume of the blood shown in the segment of the fourth image is determinable;
applying, via the computer, a filter onto the fourth image; and
generating, via the computer, a filter mask based on the fourth image.
12. The system of claim 11 , wherein the method further comprises:
performing, via the computer, a vessel segmentation process on at least one of the first image or the second image before the identifying.
13. The system of claim 12 , wherein the performing is automatically triggered.
14. The system of claim 11 , wherein the vessel comprises a diameter of about one centimeter or less.
15. The system of claim 11 , wherein the identifying is based on a vesselness filter and a pre-defined region of interest, wherein the vesselness filter filters based on a set of eigenvalues of a Hessian matrix of the third image, wherein the third image is modified such that the region of interest is positioned in a predefined area.
16. The system of claim 11 , wherein the voxel intensity mean value is based on a highest voxel intensity range in the segment, wherein the range comprises a top 40% of voxel intensities.
17. The system of claim 16 , wherein the range comprises a top 33% of voxel intensities.
18. The system of claim 16 , wherein the range comprises a top 25% of voxel intensities.
19. The system of claim 11 , wherein the third image is a cerebral blood volume map, wherein the filter is based on at least one of a performance of an expectation-maximization segmentation, or a fitting of a bimodal Gaussian curve to a histogram of data in accordance with the third image.
20. The system of claim 11 , wherein the filter mask is a binary mask, and wherein the method further comprises:
applying, via the computer, the binary mask to the third image; and
mapping, via the computer, based on the applying, the third image according to a change in a transverse relaxation time induced via an input of a contrast agent.
21. A non-transitory computer-readable storage device storing a set of instructions for execution via a processing circuit to implement a method, wherein the method comprises:
identifying, via a computer, a vessel in a third image, wherein the third image is obtained from a subtraction of a second image from a first image, wherein the second image and the first image are aligned within an imaging space, wherein the first image is post-contrast, wherein the second image is pre-contrast;
determining, via the computer, a voxel intensity mean value of a segment of the vessel in the third image based on a highest voxel intensity in the segment of the vessel such that the voxel intensity mean value indicates at least one of a content of a blood or a flow of the blood in the segment of the vessel;
obtaining, via the computer, a fourth image from a division of the third image by the voxel intensity mean value such that a percentage of a volume of the blood shown in the segment of the fourth image is determinable;
applying, via the computer, a filter onto the fourth image; and
generating, via the computer, a filter mask based on the fourth image.
22. The computer-readable storage device of claim 21 , wherein the method further comprises:
performing, via the computer, a vessel segmentation process on at least one of the first image or the second image before the identifying.
23. The computer-readable storage device of claim 22 , wherein the performing is automatically triggered.
24. The computer-readable storage device of claim 21 , wherein the vessel comprises a diameter of about one centimeter or less.
25. The computer-readable storage device of claim 21 , wherein the identifying is based on a vesselness filter and a pre-defined region of interest, wherein the vesselness filter filters based on a set of eigenvalues of a Hessian matrix of the third image, wherein the third image is modified such that the region of interest is positioned in a predefined area.
26. The computer-readable storage device of claim 21 , wherein the voxel intensity mean value is based on a highest voxel intensity range in the segment, wherein the range comprises a top 40% of voxel intensities.
27. The computer-readable storage device of claim 26 , wherein the range comprises a top 33% of voxel intensities.
28. The computer-readable storage device of claim 26 , wherein the range comprises a top 25% of voxel intensities.
29. The computer-readable storage device of claim 21 , wherein the third image is a cerebral blood volume map, wherein the filter is based on at least one of a performance of an expectation-maximization segmentation, or a fitting of a bimodal Gaussian curve to a histogram of data in accordance with the third image.
30. The computer-readable storage device of claim 21 , wherein the filter mask is a binary mask, and wherein the method further comprises:
applying, via the computer, the binary mask to the third image; and
mapping, via the computer, based on the applying, the third image according to a change in a transverse relaxation time induced via an input of a contrast agent.