PATIENT-SPECIFIC IMAGE-BASED COMPUTATIONAL MODELING AND TECHNIQUES FOR HUMAN HEART SURGERY OPTIMIZATION

• US 20080319308A1
• Filed: 05/22/2008
• Published: 12/25/2008
• Est. Priority Date: 05/22/2007
• Status: Active Grant

• Current Assignee: Worcester Polytechnic Institute
• Original Assignee: Worcester Polytechnic Institute
• Inventors: Tang, Dalin
• Granted Patent: US 8,718,944 B2
• Time in Patent Office: Days
• US Class Current: 600/416
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging

Abstract

A method for determining cardiac status comprises for a given patient, constructing a patient-specific, three-dimensional, computational model of the patient'"'"'s heart; and executing the constructed computational model, said executing generating a quantitative analysis of cardiac function. A method of performing cardiac surgeries comprises: a) assessing surgical options based on a patient-specific, three-dimensional, computational model of a patient'"'"'s heart; and b) performing surgery based on one or more of the surgical options. A computer system comprises: a) a data source containing data of a patient'"'"'s heart; b) a modeler coupled to receive data from the data source, the modeler generating a patient-specific, three-dimensional, computational model of the heart based on the heart data; and c) a processor routine for computationally providing information about a certain cardiac function using the three-dimensional heart model and for applying computational, quantitative analysis of the cardiac function, wherein the quantitative analysis of the cardiac function provides an assessment for surgical options, optimizing surgical techniques, or predicting outcomes.

Claims

1. A method for determining cardiac status, comprising:

for a given patient,

constructing a patient-specific, three-dimensional, computational model of the patient'"'"'s heart; and

executing the constructed computational model, said executing generating a quantitative analysis of cardiac function.

2. The method of claim 1, wherein the step of constructing comprises basing the model on patient-specific data including one or more of the data from ventricle morphology, cardiac motion, blood flow, material properties and pressure and volume conditions.

3. The method of claim 2, further comprising providing one or more of the patient-specific data before constructing the model.

4. The method of claim 3, wherein the step of providing one or more of the patient-specific data comprises acquiring the one or more patient-specific data using cardiac magnetic resonance.

5. The method of claim 1, further comprising constructing a three-dimensional, computational model of a healthy heart to serve as baseline control.

6. The method of claim 1, wherein the model includes any one or combination of:

right ventricle, left ventricle, and patch and scar tissues.

7. The method of claim 1, wherein the model includes fluid-structure interactions.

8. The method of claim 7, wherein the model further includes right ventricle/left ventricle/patch/scar interactions.

9. The method of claim 1, wherein the model includes valve mechanics.

10. The method of claim 9, wherein the valve mechanics comprise one or both of flow and solid stress/strain results.

11. The method of claim 1, wherein the model includes pulmonary regurgitation.

12. The method of claim 1, wherein the model includes fiber orientation and single-, double-, or multiple-layer anisotropic models.

13. The method of claim 12, wherein the anisotropic models comprise one or both of passive and active anisotropic models.

14. The method of claim 1, wherein the model includes an active contraction model.

15. The method of claim 14, wherein the step of constructing comprises introducing an external force field which is tied to fiber structure and orientations, thereby adding the active contraction model.

16. The method of claim 1, further comprising validating the model with patient-specific data.

17. The method of claim 16, further comprising assessing surgical options based on the validated model, wherein the validated model includes localized flow and stress/strain information.

18. The method of claim 17, wherein the step of assessing comprises computationally designing the surgical options.

19. The method of claim 17, further comprising constructing predictive models of post-operative outcome for one or more of the surgical options.

20. The method of claim 19, wherein the predictive models comprise one or more patch models.

21. The method of claim 19, further comprising performing surgery based on one or more of the surgical options.

22. The method of claim 21, further comprising providing post-surgery data and validating or adjusting the predictive models based on the post-surgery data.

23. A method of performing cardiac surgeries, comprising:

a) assessing surgical options based on a patient-specific, three-dimensional, computational model of a patient'"'"'s heart; and

b) performing surgery based on one or more of the surgical options.

24. A computer system, comprising:

a) a data source containing data of a patient'"'"'s heart;

b) a modeler coupled to receive data from the data source, the modeler generating a patient-specific, three-dimensional, computational model of the heart based on the heart data; and

c) a processor routine for computationally providing information about a certain cardiac function using the three-dimensional heart model and for applying computational, quantitative analysis of the cardiac function, wherein the quantitative analysis of the cardiac function provides an assessment for surgical options, optimizing surgical techniques, or predicting outcomes.

25. The computer system of claim 24, wherein the heart data includes one or more patient-specific data comprising:

ventricle morphology, cardiac motion, blood flow, material properties and pressure and volume conditions.

26. The computer system of claim 25, wherein the one or more patient-specific data are acquired using cardiac magnetic resonance.

27. The computer system of claim 24, wherein the model includes any one or combination of:

right ventricle, left ventricle, and patch and scar tissues.

28. The computer system of claim 24, wherein the model includes fluid-structure interactions.

29. The computer system of claim 28, wherein the model further includes right ventricle/left ventricle/patch/scar interactions.

30. The computer system of claim 24, wherein the model includes valve mechanics.

31. The computer system of claim 30, wherein the valve mechanics comprise one or both of flow and solid stress/strain results.

32. The computer system of claim 24, wherein the model includes pulmonary regurgitation.

33. The computer system of claim 24, wherein the model includes fiber orientation and single-, double-, or multiple-layer anisotropic models.

34. The computer system of claim 33, wherein the anisotropic models comprise one or both of passive and active anisotropic models.

35. The computer system of claim 24, wherein the model includes an active contraction model.

36. The computer system of claim 35, wherein the active contraction model is added by introducing an external force field which is tied to fiber structure and orientations.