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

Patent metadata

Field	Value
Publication number	US-9277970-B2
Application number	US-201313946661-A
Country	US
Kind code	B2
Filing date	Jul 19, 2013
Priority date	Jul 19, 2012
Publication date	Mar 8, 2016
Grant date	Mar 8, 2016

Abstract

A method and system for patient-specific planning and guidance of an ablation procedure for cardiac arrhythmia is disclosed. A patient-specific anatomical heart model is generated based on pre-operative cardiac image data. The patient-specific anatomical heart model is registered to a coordinate system of intra-operative images acquired during the ablation procedure. One or more ablation site guidance maps are generated based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure. The ablation site guidance maps may include myocardium diffusion and action potential duration maps. The ablation site guidance maps are generated using a computational model of cardiac electrophysiology which is personalized by fitting parameters of the cardiac electrophysiology model using the intra-operative patient-specific measurements. The ablation site guidance maps are displayed by a display device during the ablation procedure.

First claim

The invention claimed is:

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

Assignees

• Mansi Tommaso
• Ecabert Olivier
• Rapaka Saikiran
• Georgescu Bogdan
• Kamen Ali
• Comaniciu Dorin
• Siemens Ag

Inventors

• Mansi Tommaso
• Ecabert Olivier
• Rapaka Saikiran
• Georgescu Bogdan
• Kamen Ali
• Comaniciu Dorin

Classifications

• A61N7/022

  intracavitary · CPC title

• A61B2018/00357

  Endocardium · CPC title

• A61B2034/104

  Modelling the effect of the tool, e.g. the effect of an implanted prosthesis or for predicting the effect of ablation or burring · CPC title

• A61B2090/3764

  with a rotating C-arm having a cone beam emitting source · CPC title

• A61B2018/00577

  Ablation · CPC title