Detection of scar and fibrous cardiac zones

• US 10,646,183 B2
• Filed: 01/08/2015
• Issued: 05/12/2020
• Est. Priority Date: 01/10/2014
• Status: Active Grant

• Current Assignee: Shlomo Ben-Haim, Tylerton International Holdings Inc.
• Original Assignee: Tylerton International Inc.
• Inventors: Ben-Haim, Shlomo
• Primary Examiner(s): Deodhar, Omkar A
• Application Number:
  US15/110,756
• Publication Number:
  US 20160331337A1
• Time in Patent Office: 1,951 Days
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/465 adapted to display user selection data, e.g. graphical user interface, icons or menus
• CPC Class Codes: A61B 6/466 adapted to display 3D data
• CPC Class Codes: A61B 6/486 involving generating temporal series of image data
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/5211 involving processing of medical diagnostic data
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/5235 combining images from the same or different ionising radiation imaging techniques, e.g. PET and CT
• CPC Class Codes: A61B 6/5288 involving retrospective matching to a physiological signal
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10076 4D tomography; Time-sequential 3D tomography
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10108 Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method of tissue recognition of tissue type in a heart or other tissue, comprising:

• • (i) injecting a patient with a radioactive tracer;
  • (ii) collecting radiation emitted from cardiac tissue
  • (iii) associating said collected radiation with a wall of the heart; and
  • (iv) analyzing said associated radiation to recognize fibrous tissue in the heart wall. Optionally, the resolution of the association is better than 5 mm.

Claims

1. A method of tissue recognition of tissue type in a heart, comprising:

(i) providing radioactive emission data of cardiac tissue;

(ii) associating said radioactive emission data with a wall of the heart; and

(iii) analyzing said radioactive emission data associated with the wall of the heart to recognize fibrous tissue in the heart wall;

wherein the associating comprises;

selecting emission locations of the radioactive emission data that correspond with locations of a model of the wall of the heart, and

reconstructing a nuclear image of radioactive emissions at locations of the heart wall, using values of emission data from the selected emission locations; and

wherein the analyzing comprises;

estimating heart wall thickness in the heart wall locations of the nuclear image,

scaling the values of the emissions of emission data in the nuclear image, using the estimated heart wall thicknesses, to produce a wall-thickness normalized nuclear emission image of the heart wall, and

categorizing at least one of the heart wall locations as comprising an intermixing of fibrotic tissue and living muscle, based on the wall-thickness normalized nuclear emission image of the heart wall.

2. A method according to claim 1, wherein said analyzing comprises categorizing a shape of a fibrous zone in said heart wall.

3. A method according to claim 1, wherein said analyzing comprises identifying one or more gaps between fibrous zones in said heart wall.

4. A method according to claim 3, wherein said one or more gaps comprises a gap smaller than 5 mm in minimal width.

5. A method according to claim 1, wherein said analyzing comprises identifying one or more fibrous zones with a maximal extent of less than 30 millimeters.

6. A method according to claim 1, wherein said analyzing comprises identifying one or more fibrous zones in an atrial wall.

7. A method according to claim 1, wherein said analyzing comprises identifying one or more fibrous zones that do not reach a full wall thickness.

8. A method according to claim 1, wherein said analyzing comprises identifying one or more fibrous zones that are inside a wall.

9. A method according to claim 1, comprising generating an image of fibrous zones in at least a portion of said heart using the wall-thickness normalized nuclear emission image of the heart wall.

10. A method according to claim 9, wherein said image of fibrous zones has a resolution of better than 5 millimeters.

11. A method according to claim 9, wherein said image of fibrous zones distinguishes between fibrous tissue in different layers of the heart wall.

12. A method according to claim 9, wherein said image of fibrous zones distinguishes between different degrees of fibrosis.

13. A method according to claim 9, wherein said generating comprises normalizing said emission data non-uniformly for different sections of said wall.

14. A method according to claim 9, comprising using said image of fibrous zones for real-time navigation in the body.

15. A method according to claim 1, wherein said analyzing comprises assessing a risk for atrial fibrillation.

16. A method according to claim 1, wherein said analyzing comprises assessing a risk for ventricular arrhythmia.

17. A method according to claim 1, wherein said analyzing comprises planning a correction of an ablation procedure.

18. A method according to claim 1, wherein said associating comprises using a model of said wall.

19. A method according to claim 18, wherein said model is generated from a structural image of said heart.

20. The method of claim 1, wherein the categorizing comprises identifying a location comprising intermixing fibrotic tissue and living muscle occurring throughout an entire thickness of the heart wall location.

21. The method of claim 1, wherein the categorizing comprises estimating, for a plurality of the at least one of the heart wall locations, a corresponding plurality of different graduations of intermixing of fibrotic tissue and living muscle, each graduation indicating a different graduation of fibrosis comprising at least some fibrotic tissue and some living muscle.

22. The method of claim 1, wherein the categorizing comprises distinguishing a plurality of separate layers of the at least one of the heart wall locations, including at least one layer identified as intermixing fibrotic tissue and living muscle.

23. The method of claim 22, wherein the categorizing comprises identifying at least one of the plurality of separate layers located in-between two others of the plurality of separate layers as intermixing fibrotic tissue and living muscle.

24. The method of claim 22, wherein the scaling is performed separately for each of the plurality of separate layers.

25. The method of claim 1, wherein the categorizing comprises identifying a location comprising intermixing fibrotic tissue and living muscle, distinct from a non-transmural scar.

26. The method of claim 1, wherein the model of the heart wall comprises a wall thickness parameter specified with a different value for each of at least 10 locations of the heart wall.

27. A method of tissue recognition of tissue type in a heart, comprising:

(i) providing radioactive emission data of cardiac tissue;

(ii) associating said radioactive emission data with respective positions on a wall of the heart;

(iii) analyzing said radioactive emission data associated with the wall of the heart to recognize fibrous tissue in the heart wall; and

(iv) producing an image showing fibrotic areas of the heart;

wherein the associating comprises using a model of said wall generated with thicknesses personalized for the heart.

28. The method of claim 27, wherein the thickness are personalized using a structural image of said heart.

29. The method of claim 27, wherein the model of the heart wall comprises a wall thickness parameter specified with a different value for each of at least 10 locations of the heart wall.

31. The method of claim 27, wherein the differentiating comprises identification of separate layers of the wall of the heart.

30. A method of tissue recognition of tissue type in a heart, comprising:

(i) providing radioactive emission data of cardiac tissue;

(ii) associating said radioactive emission data with respective positions on a wall of the heart;

(iii) analyzing said radioactive emission data associated with the wall of the heart to recognize fibrous tissue in the heart wall; and

(iv) producing an image showing fibrotic areas of the heart;

wherein the analyzing and the image produced differentiate non-transmural scar tissue from fibrosis.