Method and system for fast patient-specific cardiac electrophysiology simulations for therapy planning and guidance

Patent metadata

Field	Value
Publication number	US-10296809-B2
Application number	US-201313780230-A
Country	US
Kind code	B2
Filing date	Feb 28, 2013
Priority date	Feb 28, 2012
Publication date	May 21, 2019
Grant date	May 21, 2019

Abstract

A method and system for patient-specific cardiac electrophysiology is disclosed. Particularly, a patient-specific anatomical model of a heart is generated from medical image data of a patient, a level-set representation of the patient-specific anatomical model is generated of the heart on a Cartesian grid; and a transmembrane action potential at each node of the level-set representation of the of the patient-specific anatomical model of the heart is computed on a Cartesian grid.

First claim

The invention claimed is:

1. A method for patient-specific cardiac electrophysiology computations, comprising:
generating a patient-specific anatomical model of a heart from medical image data of a patient;
identifying at least one key electrophysiological structures of interest from the patient-specific anatomical model and medical image data;
generating a level-set representation of the patient-specific anatomical model of the heart on a Cartesian grid; and
computing a transmembrane action potential on at least one node of the level-set representation of the patient-specific anatomical model of the heart, wherein computing the transmembrane action potential is based at least in part on a stimulus current input as an initial condition.

2. The method of claim 1 , wherein the step of generating a patient-specific anatomical model of a heart from medical image data of a patient comprises:
detecting a patient-specific left ventricle endocardium model, a patient-specific right ventricle endocardium model, and a patient-specific epicardium model in the medical image data; and
fusing the left ventricle endocardium model, the right ventricle endocardium model and the epicardium model into a single bi-ventricular volumetric mesh.

3. The method of claim 2 , wherein the step of generating a patient-specific anatomical model of a heart from medical image data of a patient further comprises:
mapping spatial information corresponding to at least one of scars, grey zones, or fibrosis identified from medical-image data and other clinical measurements onto the bi-ventricular volumetric mesh.

4. The method of claim 2 , wherein the step of generating a patient-specific anatomical model of a heart from medical image data of a patient further comprises:
registering a tensor field of an in-vivo diffusion tensor magnetic resonance image of cardiac fibers to the bi-ventricular volumetric mesh.

5. The method of claim 2 , wherein the step of generating a patient-specific anatomical model of a heart from medical image data of a patient further comprises:
generating a global model of fiber architecture based on the bi-ventricular volumetric mesh and nominal values for fiber elevation angle distribution.

6. The method of claim 1 , wherein the step of computing the transmembrane action potential on at least one node of the level-set representation of the patient-specific anatomical model of the heart further comprising:
solving at least one monodomain electrophysiology model using a Lattice-Boltzmann method on at least one node of the level-set representation on a Cartesian grid.

7. The method of claim 1 , wherein the step of generating a level-set representation of the patient-specific anatomical model of the heart on a Cartesian grid comprises:
discretizing a domain of the patient-specific model of the heart using a Cartesian lattice in which each node is connected via edges to a predetermined number of neighboring nodes.

8. The method of claim 1 , wherein the step of computing the transmembrane action potential on at least one node of the level-set representation of the patient-specific anatomical model of the heart further comprises:
at each of a plurality of time steps:
for at least one node of the level-set representation of the patient-specific anatomical model of the heart on a Cartesian grid,
calculating collisions of particles for each edge connected to the node based on a distribution function calculated for each edge;
calculating the transmembrane action potential at the node based on the collisions of particles calculated at the node;
updating all cellular model-dependent internal variables at the node;
updating the distribution function for each edge of the node to represent streaming of a particle traveling along each of the edges to a neighboring node; and
applying optional Dirichlet boundary conditions.

9. The method of claim 8 , wherein the step of calculating collisions of particles for each edge connected to the node based on a distribution function calculated for each edge comprises:
calculating collisions on each edge connected to the node as:
f i =f i −A ij ( f j −ω j υ)+δ tω i R (υ, { h })

where f i is the distribution function that represents a probability of finding a particle traveling along the i th edge e i connected to the node, A=(A ij ) i,j ∈ is a collision matrix that relaxes the distribution function f i towards a local potential value, υ, R(v, {h}) is a model-dependent term for electrophysiological currents, which depends on a model-dependent set of internal variables {h}, and ω i and ω j are weighting factors for edges e i and e j , respectively.

10. The method of claim 9 , wherein the collision matrix A is defined as A=M −1 SM,
where M is a matrix designed to transform the distribution functions into a vector of moments and S −1 is a matrix of relaxation times corresponding to the moments, and M and S −1 are respectively defined as:

M
=

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1

1

1

1

1

1

1

0

-
1

0

0

0

0

0

0

0

1

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1

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0

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0

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Assignees

• Rapaka Saikiran
• Mansi Tommaso
• Georgescu Bogdan
• Kamen Ali
• Comaniciu Dorin
• Siemens Healthcare Gmbh

Inventors

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

Classifications

• G06V10/755

  Deformable models or variational models, e.g. snakes or active contours · CPC title

• G06T7/143 Primary

  involving probabilistic approaches, e.g. Markov random field [MRF] modelling · CPC title

• G06F2218/00

  Aspects of pattern recognition specially adapted for signal processing · CPC title

• G06T2207/30048

  Heart; Cardiac · CPC title

• G06T2207/10076

  4D tomography; Time-sequential 3D tomography · CPC title