System and method for facilitating cardiac intervention

• US 7,327,862 B2
• Filed: 04/30/2002
• Issued: 02/05/2008
• Est. Priority Date: 04/30/2001
• Status: Expired due to Fees

• Current Assignee: Capital Southwest Corporation
• Original Assignee: Chase Medical LP
• Inventors: Murphy, Gregory, Suresh, Mitta, Davis, Albert
• Primary Examiner(s): Mehta; Bhavesh M
• Assistant Examiner(s): BHATNAGAR, ANAND P
• Application Number:
  US10/135,465
• Publication Number:
  US 20050020929A1
• Time in Patent Office: 2,107 Days
• Field of Search: 382/100, 382128-133, 128/922, 378/4
• US Class Current: 382/128
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G09B 23/28 for medicine
• CPC Class Codes: G09B 23/30 Anatomical models G09B23/281 - G09B23/288 take precedence
• CPC Class Codes: Y10S 128/922 including image analysis

Abstract

One embodiment discloses a computerized method of facilitating cardiac intervention, comprising inputting patient data, creating a computerized interactive model of a diseased heart based on the patient data, wherein the model comprises structural elements, simulating at least one proposed cardiac intervention treatment by adding or deleting structural elements to the model, and determining the effects of the proposed cardiac simulation upon the entire model. Simulations may be repeated to allow the user to determine an optimal cardiac intervention. Additionally, a template may be created from the model to use as a guide during the cardiac intervention.

Claims

1. A method of diagnosing a human heart, comprising:

providing to a computer system a plurality of first images of heart tissue from the heart;

using the plurality of first images to create at least one second image of human heart tissue using the computer system, comprising;

creating endocardial and epicardial left ventricle wall contours;

automatically segmenting at least a portion of left ventricle wall endocardial contours of at least two of the provided images of heart tissue into a selected number of endocardial left ventricle wall points;

automatically segmenting at least a portion of left ventricle wall epicardial contours of at least two of the provided images of heart tissue into a selected number of epicardial left ventricle wall points; and

automatically using endocardial left ventricle wall points and epicardial left ventricle wall points to create a model of at least a portion of the left ventricle wall over one or more periods of time;

wherein the second image comprises one or more structural elements; and

comparing at least one structural element to one or more heart reference structural elements in a database to assess a state of the heart.

2. The method of claim 1, wherein at least one of the reference structural elements comprises an image.

3. The method of claim 1, wherein at least one of the reference structural elements comprises a portion of an image.

4. The method of claim 1, wherein at least one of the reference structural elements comprises a physiological factor.

5. The method of claim 1, wherein at least one of the reference structural elements comprises a numerical element.

6. The method of claim 1, wherein at least one of the reference structural elements comprises a numerical element derived at least in part from at least a portion of an image.

7. The method of claim 1, wherein comparing at least one structural element comprises using the computer system to perform the comparison.

8. The method of claim 1, wherein the database comprises data from one or more surgical procedures.

9. The method of claim 1, wherein the computer system divides the plurality of first images into a plurality of sections.

10. The method of claim 1, further comprising extrapolating at least a portion of at least one structural element from the plurality of first images of human heart tissue provided to the computer system.

11. The method of claim 1, further comprising using the plurality of first images to create at least a second image, wherein at least a portion of the second image appears at least four-dimensional.

12. The method of claim 11, wherein one of the four-dimensions comprises time.

13. The method of claim 11, wherein at least one of the four-dimensions comprises at least one physiological factor.

14. The method of claim 13, wherein at least one physiological factor comprises hormone B-type natriuretic peptide.

15. The method of claim 1, further comprising creating one or more images of the assessed state of the heart.

16. The method of claim 15, wherein at least one image of the assessed state of the heart comprises at least a portion appearing at least three-dimensional.

17. The method of claim 15, wherein at least one image of the assessed state of the heart comprises color gradients.

18. The method of claim 1, further comprising creating endocardial and epicardial left ventricle wall contours automatically.

19. The method of claim 1, further comprising using the model of the portion of the left ventricle wall over time to assess left ventricular wall thickness over time.

20. The method of claim 1, further comprising automatically comparing the model of at least a portion of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database.

21. The method of claim 1, further comprising automatically comparing the model of at least a portion of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database, wherein the reference left ventricle wall comprises an equivalent number of corresponding portions such that the model may be directly compared to the reference left ventricle wall.

22. The method of claim 1, further comprising automatically comparing the model of at least a portion of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database, wherein the reference left ventricle wall comprises the selected number of epicardial left ventricle wall points and the selected number of endocardial left ventricle wall points.

23. The method of claim 1, further comprising using the model to automatically assess wall thickness over time of the left ventricle.

24. The method of claim 1, further comprising using the model to automatically assess areas of abnormal movement in the left ventricle.

25. The method of claim 1, further comprising assessing one or more portions of the left ventricle to recommend for exclusion during a surgical procedure.

26. The method of claim 1, further comprising:

automatically comparing the model of at least a portion of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database; and

automatically assessing a state of the human heart.

27. The method of claim 1, further comprising:

automatically comparing the model of at least a portion of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database;

automatically assessing a state of the human heart; and

automatically creating one or more images of the assessed state of the heart.

28. The method of claim 1, further comprising automatically comparing the model of at least a partition of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database, wherein the reference left ventricle wall is derived from clinical data.

29. The method of claim 1, further comprising automatically comparing the model of at least a partition of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database, wherein the reference left ventricle wall is derived from one or more surgical procedures.

30. The method of claim 1, wherein the model appears at least three-dimensional.

31. The method of claim 1, wherein the model appears at least four-dimensional.

32. A method of diagnosing a human heart, comprising:

providing two or more sectional views of heart tissue from the heart to a computer system;

creating endocardial and epicardial left ventricle wall contours;

automatically segmenting at least a portion of left ventricle wall endocardial contours of at least two of the sectional views into a selected number of endocardial left ventricle wall points;

automatically segmenting at least a portion of left ventricle wall epicardial contours of at least two of the sectional views into a selected number of epicardial left ventricle wall points;

automatically using endocardial left ventricle wall points and epicardial left ventricle wall points to create a model of at least a portion of the left ventricle wall over one or more periods of time; and

automatically comparing the model of at least a portion of the left ventricle wall with one or more corresponding portions of reference left ventricle wall in a database; and

automatically assessing a state of the human heart.

33. The method of claim 32, further comprising creating endocardial and epicardial left ventricle wall contours automatically.

34. The method of claim 32, further comprising using the model of the portion of the left ventricle wall over time to assess left ventricular wall thickness over time.

35. The method of claim 32, wherein the reference left ventricle wall comprises an equivalent number of corresponding portions such that the model may be directly compared to the reference left ventricle wall.

36. The method of claim 32, wherein the reference left ventricle wall comprises the selected number of epicardial left ventricle wall points and the selected number of endocardial left ventricle wall points.

37. The method of claim 32, further comprising using the model to automatically assess wall thickness over time of the left ventricle.

38. The method of claim 32, further comprising using the model to automatically assess areas of abnormal movement in the left ventricle.

39. The method of claim 32, further comprising assessing one or more portions of the left ventricle to recommend for exclusion during a surgical procedure.

40. The method of claim 32, further comprising automatically creating one or more images of the assessed state of the heart.

41. The method of claim 32, wherein the reference left ventricle wall is derived from clinical data.

42. The method of claim 32, wherein the reference left ventricle wall is derived from one or more surgical procedures.

43. The method of claim 32, wherein the model appears at least three-dimensional.

44. The method of claim 32, wherein the model appears at least four-dimensional.