System and method for patient specific planning and guidance of ablative procedures for cardiac arrhythmias

1. A method for patient-specific guidance of an ablation procedure, comprising:

registering a patient-specific anatomical heart model extracted from pre-operative cardiac image data to a coordinate system of an intra-operative image acquired during the ablation procedure;

estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure; and

generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology.

2. The method of claim 1 , further comprising:

displaying the one or more ablation site guidance maps on a display device during the ablation procedure.

3. The method of claim 1 , further comprising:

receiving the pre-operative cardiac image data; and

generating the patient-specific anatomical heart model based on the pre-operative cardiac image data.

4. The method of claim 3 , wherein generating the patient-specific anatomical heart model based on the pre-operative cardiac image data comprises:

extracting a multi-component patient-specific heart morphology model from the pre-operative cardiac image data;

fusing the multi-component patient-specific heart morphology model into a single heart model and tagging surface elements of the single heart model into surface zones; and

generating a model of myocardium fiber architecture based on the single heart model.

5. The method of claim 4 , wherein generating the patient-specific anatomical heart model based on the pre-operative cardiac image data further comprises:

segmenting scar tissue in the pre-operative image data; and

mapping the segmented scar tissue to the volumetric single heart model.

6. The method of claim 1 , wherein registering a patient-specific anatomical heart model extracted from pre-operative cardiac image data to a coordinate system of an intra-operative image acquired during the ablation procedure comprises:

registering the patient-specific anatomical heart model to an intra-operative three-dimensional rotational angiography image acquired during the ablation procedure.

7. The method of claim 6 , wherein registering the patient-specific anatomical heart model to an intra-operative three-dimensional rotational angiography image acquired during the ablation procedure comprises:

calculating a probability map of a cardiac pericardium in the three-dimensional rotational angiography image using a machine learning algorithm;

calculating a deformation that maps the pericardium surface mesh of the patient-specific anatomical heart model to the coordinate system of the three-dimensional rotational angiography image using an optimization algorithm that maximizes the probability map along the surface mesh;

calculating a dense deformation field by extrapolating the deformation of every node;

registering the patient-specific anatomical heart model to the coordinate system of the three-dimensional rotational angiography image using the dense deformation field; and

re-orienting myocardium fibers of the patient-specific anatomical heart model using a local Jacobian matrix of the dense deformation field.

8. The method of claim 1 , wherein registering a patient-specific anatomical heart model extracted from pre-operative cardiac image data to a coordinate system of an intra-operative image acquired during the ablation procedure comprises:

registering the patient-specific anatomical heart model to the coordinate system of the intra-operative image based on spatial fiducials provided in the intra-operative image by an endocardial mapping system.

9. The method of claim 1 , wherein registering a patient-specific anatomical heart model extracted from pre-operative cardiac image data to a coordinate system of an intra-operative image acquired during the ablation procedure comprises:

calculating a transformation to register a component of the patient-specific anatomical heart model to the coordinate system of the intra-operative image; and

transforming the patient-specific anatomical entire heart model using the calculated transformation.

10. The method of claim 1 , wherein estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure comprises:

generating a Cartesian grid domain using the registered patient-specific anatomical heart model; and

calculating transmembrane potential variation over time at each of a plurality of nodes within the myocardium in the Cartesian grid domain by computing a solution of a cardiac electrophysiology model for each of the plurality of nodes using a Lattice-Boltzmann method for electrophysiology.

11. The method of claim 10 , wherein estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure further comprises:

estimating parameters of the cardiac electrophysiology model using an inverse problem approach.

12. The method of claim 11 , wherein estimating parameters of the cardiac electrophysiology model using an inverse problem approach comprises:

calculating a cost function that compares the depolarization and repolarization times calculated the plurality of nodes using the cardiac electrophysiology model with depolarization and repolarization times determined from current intra-operative electrophysiological mapping measurements of the patient acquired during the ablation procedure;

estimating electrical diffusivity and action potential duration parameters of the cardiac electrophysiology model using an inverse problem algorithm to minimize the cost function; and

re-calculating the transmembrane potential variation over time at each of the plurality of nodes within the myocardium in the Cartesian grid domain by computing a solution of a cardiac electrophysiology model with the fitted diffusivity and action potential duration parameters for each of the plurality of nodes using the Lattice-Boltzmann method for electrophysiology.

13. The method of claim 1 , further comprising:

performing virtual pacing using the patient-specific computational model of cardiac electrophysiology.

14. The method of claim 13 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology comprises:

generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology and the virtual pacing.

15. The method of claim 14 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology and the virtual pacing comprises:

generating the one or more ablation site guidance maps resulting from a computation of cardiac electrophysiology in response to the virtual pacing using the patient-specific computational model of cardiac electrophysiology.

16. The method of claim 14 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology and the virtual pacing comprises:

generating a map of ventricular tachycardia (VT) trigger point candidates based on the virtual pacing.

17. The method of claim 13 , wherein performing virtual pacing using the patient-specific computational model of cardiac electrophysiology comprises:

receiving a user selection of a spatial location of a virtual pacing catheter; and

calculating potential variation over time at a plurality of points within the myocardium using the patient-specific computational model of cardiac electrophysiology with a stimulus current added at the selected spatial location and applied at a user-defined frequency.

18. The method of claim 13 , wherein performing virtual pacing using the patient-specific computational model of cardiac electrophysiology comprises:

automatically selecting a plurality of spatial locations at which to perform virtual pacing; and

for each of the plurality of spatial locations, calculating potential variation over time at a plurality of points within the myocardium using the patient-specific computational model of cardiac electrophysiology with a stimulus current added at that spatial location and applied at one or more different frequencies.

19. The method of claim 1 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology comprises:

generating three-dimensional maps of one or more fitted parameters of the patient-specific computational model of cardiac electrophysiology over a spatial domain defined using the registered patient-specific anatomical heart model.

20. The method of claim 19 , wherein generating three-dimensional maps of one or more fitted parameters of the patient-specific computational model of cardiac electrophysiology over a spatial domain defined using the registered patient-specific anatomical heart model comprises:

generating a three-dimensional map of a tissue diffusivity parameter at each of a plurality of nodes of the spatial domain; and

generating a three-dimensional map of an action potential duration parameter at each of the plurality of nodes of the spatial domain.

21. The method of claim 1 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology comprises:

generating a time varying three-dimensional map of potentials of the plurality of nodes based on the patient-specific computational model of cardiac electrophysiology;

generating a three-dimensional map of depolarization times of the plurality of nodes based on the time varying three-dimensional map of potentials; and

generating a three-dimensional map of repolarization times of the plurality of nodes based on the time varying three-dimensional map of potentials.

22. The method of claim 1 , further comprising:

acquiring additional intra-operative patient-specific measurements during the ablation procedure;

updating the patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and the additional intra-operative patient-specific measurements acquired during the ablation procedure; and

re-generating the one or more ablation site guidance maps based on the updated patient-specific computational model of cardiac electrophysiology.

23. An apparatus for patient-specific guidance of an ablation procedure, comprising:

means for registering a patient-specific anatomical heart model extracted from pre-operative cardiac image data to a coordinate system of an intra-operative image acquired during the ablation procedure;

means for estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure; and

means for generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology.

24. The apparatus of claim 23 , further comprising:

means for displaying the one or more ablation site guidance maps on a display device during the ablation procedure.

25. The apparatus of claim 23 , further comprising:

means for generating the patient-specific anatomical heart model based on the pre-operative cardiac image data.

26. The apparatus of claim 23 , wherein the means for estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure comprises:

means for generating a Cartesian grid domain using the registered patient-specific anatomical heart model; and

means for calculating transmembrane potential variation over time at each of a plurality of nodes within the myocardium in the Cartesian grid domain by computing a solution of a cardiac electrophysiology model for each of the plurality of nodes using a Lattice-Boltzmann method for electrophysiology.

27. The apparatus of claim 26 , wherein the means for estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure further comprises:

means for estimating parameters of the cardiac electrophysiology model using an inverse problem approach.

28. The apparatus of claim 27 , wherein the means for estimating parameters of the cardiac electrophysiology model using an inverse problem approach comprises:

means for calculating a cost function that compares the depolarization and repolarization times calculated the plurality of nodes using the cardiac electrophysiology model with depolarization and repolarization times determined from current intra-operative electrophysiological mapping measurements of the patient acquired during the ablation procedure;

means for estimating electrical diffusivity and action potential duration parameters of the cardiac electrophysiology model using an inverse problem algorithm to minimize the cost function; and

means for re-calculating the transmembrane potential variation over time at each of the plurality of nodes within the myocardium in the Cartesian grid domain by computing a solution of a cardiac electrophysiology model with the fitted diffusivity and action potential duration parameters for each of the plurality of nodes using the Lattice-Boltzmann method for electrophysiology.

29. The apparatus of claim 23 , further comprising:

means for performing virtual pacing using the patient-specific computational model of cardiac electrophysiology.

30. The apparatus of claim 29 , wherein the means for generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology comprises:

means for generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology and the virtual pacing.

31. The apparatus of claim 23 , further comprising:

means for acquiring additional intra-operative patient-specific measurements during the ablation procedure;

means for updating the patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and the additional intra-operative patient-specific measurements acquired during the ablation procedure; and

means for re-generating the one or more ablation site guidance maps based on the updated patient-specific computational model of cardiac electrophysiology.

32. The non-transitory computer readable medium of claim 31 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology comprises:

generating three-dimensional maps of one or more fitted parameters of the patient-specific computational model of cardiac electrophysiology over a spatial domain defined using the registered patient-specific anatomical heart model.

33. The non-transitory computer readable medium of claim 32 , wherein generating three-dimensional maps of one or more fitted parameters of the patient-specific computational model of cardiac electrophysiology over a spatial domain defined using the registered patient-specific anatomical heart model comprises:

generating a three-dimensional map of a tissue diffusivity parameter at each of a plurality of nodes of the spatial domain; and

generating a three-dimensional map of an action potential duration parameter at each of the plurality of nodes of the spatial domain.

34. A non-transitory computer readable medium storing computer program instructions for patient-specific guidance of an ablation procedure, the computer program instructions when executed by a processor cause the processor to perform operations comprising:

registering a patient-specific anatomical heart model extracted from pre-operative cardiac image data to a coordinate system of an intra-operative image acquired during the ablation procedure;

estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure; and

generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology.

35. The non-transitory computer readable medium of claim 34 , wherein the operations further comprise:

displaying the one or more ablation site guidance maps on a display device during the ablation procedure.

36. The non-transitory computer readable medium of claim 34 , wherein the operations further comprise:

receiving the pre-operative cardiac image data; and

generating the patient-specific anatomical heart model based on the pre-operative cardiac image data.

37. The non-transitory computer readable medium of claim 34 , wherein estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure comprises:

generating a Cartesian grid domain using the registered patient-specific anatomical heart model; and

calculating transmembrane potential variation over time at each of a plurality of nodes within the myocardium in the Cartesian grid domain by computing a solution of a cardiac electrophysiology model for each of the plurality of nodes using a Lattice-Boltzmann method for electrophysiology.

38. The non-transitory computer readable medium of claim 37 , wherein estimating a patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure further comprises:

estimating parameters of the cardiac electrophysiology model using an inverse problem approach.

39. The non-transitory computer readable medium of claim 38 , wherein estimating parameters of the cardiac electrophysiology model using an inverse problem approach comprises:

calculating a cost function that compares the depolarization and repolarization times calculated the plurality of nodes using the cardiac electrophysiology model with depolarization and repolarization times determined from current intra-operative electrophysiological mapping measurements of the patient acquired during the ablation procedure;

estimating electrical diffusivity and action potential duration parameters of the cardiac electrophysiology model using an inverse problem algorithm to minimize the cost function; and

re-calculating the transmembrane potential variation over time at each of the plurality of nodes within the myocardium in the Cartesian grid domain by computing a solution of a cardiac electrophysiology model with the fitted diffusivity and action potential duration parameters for each of the plurality of nodes using the Lattice-Boltzmann method for electrophysiology.

40. The non-transitory computer readable medium of claim 34 , wherein the operations further comprise:

performing virtual pacing using the patient-specific computational model of cardiac electrophysiology.

41. The non-transitory computer readable medium of claim 40 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology comprises:

generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology and the virtual pacing.

42. The non-transitory computer readable medium of claim 41 , wherein generating one or more ablation site guidance maps based on the patient-specific computational model of cardiac electrophysiology and the virtual pacing comprises:

generating a map of ventricular tachycardia (VT) trigger point candidates based on the virtual pacing.

43. The non-transitory computer readable medium of claim 40 , wherein performing virtual pacing using the patient-specific computational model of cardiac electrophysiology comprises:

receiving a user selection of a spatial location of a virtual pacing catheter; and

calculating potential variation over time at a plurality of points within the myocardium using the patient-specific computational model of cardiac electrophysiology with a stimulus current added at the selected spatial location and applied at a user-defined frequency.

44. The non-transitory computer readable medium of claim 40 , wherein performing virtual pacing using the patient-specific computational model of cardiac electrophysiology comprises:

automatically selecting a plurality of spatial locations at which to perform virtual pacing; and

for each of the plurality of spatial locations, calculating potential variation over time at a plurality of points within the myocardium using the patient-specific computational model of cardiac electrophysiology with a stimulus current added at that spatial location and applied at one or more different frequencies.

45. The non-transitory computer readable medium of claim 34 , wherein the operations further comprise:

acquiring additional intra-operative patient-specific measurements during the ablation procedure;

updating the patient-specific computational model of cardiac electrophysiology based on the registered patient-specific anatomical heart model and the additional intra-operative patient-specific measurements acquired during the ablation procedure; and

re-generating the one or more ablation site guidance maps based on the updated patient-specific computational model of cardiac electrophysiology.