Method and system for knowledge guided hyperintensity detection and volumetric measurement

• US 6,430,430 B1
• Filed: 05/01/2000
• Issued: 08/06/2002
• Est. Priority Date: 04/29/1999
• Status: Expired due to Fees

• Current Assignee: University of South Florida (State University System of Florida)
• Original Assignee: University of South Florida (State University System of Florida)
• Inventors: Gosche, Karen M.
• Primary Examiner(s): LIN, JEOYUH
• Assistant Examiner(s): James, Ialaya G
• Application Number:
  US09/561,956
• Time in Patent Office: 827 Days
• Field of Search: 600/410, 600/407, 128/920, 128/923, 128/925, 382/294, 382/293
• US Class Current: 600/410
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/155 involving morphological operators
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/923 by comparison of patient data to other data
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

An automated method and/or system for identifying suspected lesions in a brain is provided. A processor (a) provides a magnetic resonance image (MRI) of a patient'"'"'s head, including a plurality of slices of the patient'"'"'s head, which MRI comprises a multispectral data set that can be displayed as an image of varying pixel intensities. The processor (b) identifies a brain area within each slice to provide a plurality of masked images of intracranial tissue. The processor (c) applies a segmentation technique to at least one of the masked images to classify the varying pixel intensities into separate groupings, which potentially correspond to different tissue types. The processor (d) refines the initial segmentation into the separate groupings of at least the first masked image obtained from step (c) using one or more knowledge rules that combine pixel intensities with spatial relationships of anatomical structures to locate one or more anatomical regions of the brain. The processor (e) identifies, if present, the one or more anatomical regions of the brain located in step (d) in other masked images obtained from step (c). The processor (f) further refines the resulting knowledge rule-refined images from steps (d) and (e) to locate suspected lesions in the brain.

Claims

1. A method of interpreting at least one imaging scan of a patient, comprising the step of:

(a) identifying a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle, said identifying step (a) being free of human intervention.

2. The method according to claim 1, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said method further comprising the step of:

(b) identifying a location of each cerebral region of the at least one cerebral region within a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region within a preceding imaging scan of the plurality of imaging scans, said identifying step (b) being free of human intervention.

3. The method according to claim 1, further comprising the step of:

(c) determining a volumetric measurement for the at least one cerebral region.

4. The method according to claim 1, further comprising the step of:

(d) identifying a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.

5. The method according to claim 4, wherein said identifying step (d) includes identifying white matter regions in the at least one imaging scan, eliminating therefrom voxels identifiable as at least one of white matter, a perivascular space, and cerebrospinal fluid.

6. The method according to claim 5, wherein the white matter regions include at least one of an internal capsule, a frontal lobe, an anterior temporal lobe, an anterior parietal lobe, a posterior parietal lobe, an occipital lobe, and a posterior temporal lobe.

7. The method according to claim 1, wherein said at least one cerebral region includes at least one of a caudate nucleus, a lenticular nucleus, a thalamus, a hippocampus, a periventricular ring, white matter, a brain stem, and a cerebellum.

8. The method according to claim 1, further comprising the step of generating a template unique to the patient, the template including each identified at least one cerebral region.

9. The method according to claim 8, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said method further comprising the step of:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.

10. An automated method of identifying suspected lesions in a brain, which comprises:

(a) providing a magnetic resonance image (MRI) of a patient'"'"'s head, including a plurality of slices of the patient'"'"'s head, which MRI comprises a multispectral data set that can be displayed as an image, wherein said image comprises varying pixel intensities;

(b) identifying a brain area within each slice to provide a plurality of masked images of intracranial tissue;

(c) applying a segmentation technique to at least one of the masked images to classify the varying pixel intensities into separate groupings, which potentially correspond to different tissue types;

(d) refining the initial segmentation into the separate groupings of at least a first masked image of the masked images obtained from step (c) using one or more knowledge rules that combine pixel intensities with spatial relationships of anatomical structures to locate one or more anatomical regions of the brain;

(e) if present, identifying the one or more anatomical regions of the brain located in step (d) in other masked images obtained from step (c);

(f) further refining the resulting knowledge rule-refined images from steps (d) and (e) to locate suspected lesions in the brain.

11. The method of claim 10 in which said magnetic resonance image comprises a multispectral data set including proton density weighted (PDw), T1 weighted (T1w) and T2 weighted (T2w) acqusitions.

12. The method of claim 10 in which the slices are taken in the axial, coronal, or sagittal planes of the patient'"'"'s head.

13. The method of claim 10 in which the varying pixel intensities are classified into at least three separate groupings which potentially correspond to at least three different tissue types, including a first tissue type, a second tissue type, and a third tissue type.

14. The method of claim 10, wherein said further refining step (f) comprises identifying hyperintensities in the one or more anatomical regions of the brain, and said method further comprising the step of:

(a) determining volumetric measurements of the located suspected lesions.

15. An apparatus for interpreting at least one imaging scan of a patient, comprising:

(a) first identifying means for identifying a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle, said first identifying means (a) being free of human intervention.

16. The apparatus according to claim 15, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said apparatus further comprising:

(b) second means for identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, said second identifying means (b) being free of human intervention.

17. The apparatus according to claim 16, further comprising:

(c) means for determining a volumetric measurement for the at least one cerebral region.

18. The apparatus according to claim 15, further comprising:

(d) third means for identifying a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.

19. The apparatus according to claim 15, further comprising means for generating a template unique to the patient, the template including each identified at least one cerebral region.

20. The apparatus according to claim 19, wherein the-at least one imaging scan includes a plurality of consecutive imaging scans, said apparatus further comprising:

(a) fourth means for identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the template unique to the patient.

21. A computer readable medium including instructions being executed by a computer, the instructions instructing the computer to execute an interpretation of at least one imaging scan of a patient, the instructions comprising:

(a) identifying a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle, said identifying instruction (a) being free of human intervention.

22. The computer readable medium according to claim 21, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said instructions further comprising:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, said identifying instruction (b) being free of human intervention.

23. The computer readable medium according to claim 22, wherein the instructions further comprise:

(c) determining a volumetric measurement for the at least one cerebral region.

24. The computer readable medium according to claim 21, wherein the instructions further comprise:

(d) identifying a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.

25. The computer readable medium according to claim 21, wherein said instructions further comprise generating a template unique to the patient, the template including each identified at least one cerebral region.

26. The computer readable medium according to claim 25, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said instructions further comprising:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.

27. The computer system according to claim 21, wherein the instructions further comprise:

(d) identifying a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.

28. A computer system for interpreting at least one imaging scan of a patient, said computer system comprising:

a processor;

a memory storing a computer program controlling operation of said processor, said program including instructions for causing the processor to effect;

(a) identifying a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle, said identifying instruction (a) being free of human intervention.

29. The computer system according to claim 28, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said instructions further comprising:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, said identifying instruction (b) being free of human intervention.

30. The computer system according to claim 29, wherein the instructions further comprise:

(c) determining a volumetric measurement for the at least one cerebral region.

31. The computer system according to claim 28, wherein said instructions further comprise generating a template unique to the patient, the template including each identified at least one cerebral region.

32. The computer readable medium according to claim 31, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said instructions further comprising:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.

33. The computer system according to claim 28, further comprising a computer including said processor, the computer communicatable with a user via a computer network.

34. The computer system according to claim 33, wherein said computer includes a Web server.

35. An internet appliance comprising:

a thin client programmably connected via a computer network to a single web hosting facility, the single web hosting facility including a server communicatable with a user via said thin client, said server being in communication with a processor and a computer readable medium including instructions being executed by a processor, the instructions instructing the computer to execute an interpretation of at least one imaging scan of a patient, the instructions comprising;

(a) identifying a location of at least one cerebral region in the imaging scan based, at least in part, on a relative location of a lateral ventricle, said identifying instruction (a) being free of human intervention.

36. The internet appliance according to claim 35, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said instructions further comprising:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, said identifying instruction (b) being free of human intervention.

37. The internet appliance according to claim 36, wherein the instructions further comprise:

(c) determining a volumetric measurement for the at least one cerebral region.

38. The internet appliance according to claim 35, wherein the instructions further comprise:

(d) identifying a suspected presence of white matter lesion tissue in the imaging scan based on a knowledge base relating voxel intensity distributions and a spatial relationship of cerebral anatomical structures.

39. The internet appliance according to claim 35, wherein said instructions further comprise generating a template unique to the patient, the template including each identified at least one cerebral region.

40. The internet appliance according to claim 39, wherein the at least one imaging scan includes a plurality of consecutive imaging scans, said instructions further comprising:

(b) identifying a location of each cerebral region of the at least one cerebral region in a successive imaging scan of the plurality of imaging scans based, at least in part, on a location of a corresponding cerebral region in a preceding imaging scan of the plurality of imaging scans, using the generated template.