AUTOMATED SURFACE-BASED ANATOMICAL ANALYSIS BASED ON ATLAS-BASED SEGMENTATION OF MEDICAL IMAGING

• US 20110092794A1
• Filed: 07/07/2009
• Published: 04/21/2011
• Est. Priority Date: 07/07/2008
• Status: Active Grant

• Current Assignee: Johns Hopkins University
• Original Assignee: Johns Hopkins University
• Inventors: Mori, Susumu, Miller, Michael I., Qiu, Anqi
• Granted Patent: US 9,730,615 B2
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: A61B 5/103 Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes A61B5/08 takes precedence; measuring aids for tailors A41H1/00; measuring instruments specially adapted for dentistry A61C19/04
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/501 for diagnosis of the head, e.g. neuroimaging or craniography
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20068 Projection on vertical or horizontal image axis
• CPC Class Codes: G06T 2207/20128 Atlas-based segmentation
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/46 using random fields

Abstract

A non-invasive imaging system, including: a non-invasive imaging scanner; a signal processing unit in communication with the imaging scanner to receive an imaging signal from a subject under observation; and a data storage unit in communication with the signal processing unit, wherein the data storage unit stores template data corresponding to a tissue region of the subject, and wherein the signal processing unit is adapted to generate a surface map to encode a property of a subvolume of the tissue region using the template data.

Claims

1. A non-invasive imaging system, comprising:

a non-invasive imaging scanner;

a signal processing unit in communication with said imaging scanner to receive an imaging signal from a subject under observation; and

a data storage unit in communication with said signal processing unit, wherein said data storage unit stores template data corresponding to a tissue region of said subject, and

wherein said signal processing unit is adapted to generate a surface map to encode a property of a subvolume of said tissue region using said template data.

2. The imaging system according to claim 1, further comprising:

a display apparatus in communication with at least one of said signal processing unit and said data storage unit to display the analyzed deformation pattern.

3. The imaging system according to claim 1, wherein said surface map comprises a normal surface momentum map encoding a boundary shape of said subvolume of said tissue region.

4. The imaging system according to claim 1, wherein said normal surface momentum map is modeled as a linear combination of at least one spatially localizing basis function encoding spatial shape variation information of said subvolume of said tissue region,

5. The imaging system according to claim 4, further comprising:

extracting a principal component of said linear combination of at least one spatially localizing basis function.

6. The imaging system according to claim 4, wherein said signal processing unit is further adapted to:

compare said linear combination determined for said subvolume of said tissue region from said subject with a statistical database of said linear combination previously determined for said subvolume of said tissue region from one or more groups of subjects; and

determine if said subject statistically belongs to said one or more groups of subjects.

7. The imaging system according to claim 1, wherein said tissue region is at least one of a brain, a heart, a liver, or other internal organ of said subject under observation.

8. The imaging system according to claim 1, wherein said template data incorporates at least one image from at least one subject that is different from said subject under observation.

9. The imaging system according to claim 1, wherein said template data incorporates at least one image from a previous scan of said subject under observation.

10. The imaging system according to claim 1, wherein said signal processing unit is at least partially incorporated in a workstation that is structurally separate and in communication with said imaging scanner.

11. The imaging system according to claim 1, wherein said signal processing unit is at least partially incorporated within a structure housing said imaging scanner.

12. A workstation, comprising:

a receiving engine to receive an imaging signal showing a tissue region from a subject and template data corresponding to said tissue region from at least one of a non-invasive imaging system, one or more data storage devices, a software simulation executed by one or more processors, or combinations thereof; and

a processing engine to generate a surface map to encode a property of a subvolume of said tissue region in said imaging signal using said template data.

13. The workstation according to claim 12, further comprising:

a visualization engine to display said surface map.

14. The workstation according to claim 12, wherein said receiving engine is further adapted to receive a first and second plurality of imaging signals showing said tissue region from a first and second plurality of subjects, respectively.

15. The workstation according to claim 14, wherein said processing engine is further adapted to generate a first and second set of surface maps to encode said property of said subvolume of said tissue region in said first and second set of imaging signals, respectively.

16. The workstation according to claim 15, wherein said processing engine is further adapted to determine a statistical difference between said first and second set of surface maps.

17. The workstation according to claim 16, wherein said visualization engine is further adapted to display the determined statistical difference.

18. A method of processing images, comprising:

receiving template data corresponding to a region having a subvolume and an image showing said region; and

generating a surface map to encode a property of said subvolume of said region using said template data.

19. The method according to claim 18, wherein said region is a tissue region.

20. The method according to claim 18, wherein said template data and said image are received from at least one of a non-invasive imaging system, one or more data storage devices, a software simulation executed by one or more processors, or combinations thereof.

21. The method according to claim 18, wherein said image is from a subject under observation.

22. The method according to claim 18, wherein said surface map comprises a normal surface momentum map encoding a boundary shape of said subvolume of said region.

23. The method according to claim 22, wherein said normal surface momentum map is modeled as a linear combination of at least one spatially localizing basis function encoding spatial shape variation information of said subvolume of said region.

24. The method according to claim 23, wherein said localizing basis function is a Laplace-Beltrami (LB) function.

25. The method according to claim 23, further comprising:

comparing said linear combination determined for said subvolume of said tissue region in said image from said subject with a statistical database of said linear combination previously determined for said subvolume of said tissue region in existing images from one or more groups; and

determining if said image is statistically correlated with said existing images from said one or more groups.

26. The method according to claim 23, further comprising:

extracting a principal component of said linear combination of at least one spatially localizing basis function.

27. The method according to claim 26, further comprising:

extracting a first set of principal components of said linear combination modeling a first set of normal surface momentum maps that encode said property of said subvolume of said region in a first plurality of images from a first plurality of subjects;

extracting a second set of principal components of said linear combination modeling a second set of normal surface momentum maps that encode said property of said subvolume of said region in a second plurality of images from a second plurality of subjects; and

determining a statistical difference between the extracted first and second set of principal components.

28. The method according to claim 27, wherein said statistical difference signifies a physiological difference between said first and second plurality of subjects.

29. A computer-readable medium containing software, which software when executed by a computer, causes the computer to implement the method according to claim 18.