Cardiac Electrophysiology Simulation via Distance-Based Diffusion
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Solution Overview
Problem
Current computational models for simulating cardiac electrophysiology struggle to achieve real-time patient-specific modeling of the electrical conduction system of the heart, balancing accuracy and computational cost, which is crucial for effective planning and guidance of electrophysiology interventions.
Innovation Solution
The method employs the Lattice Boltzmann method for Electrophysiology (LBM-EP) to simulate cardiac electrophysiology using a coarse grid, with a sub-grid accurate representation of the electrical conduction system by assigning high diffusion coefficients to grid cells based on the distance from the endocardium, allowing for real-time or near real-time simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed anatomical structures (Purkinje system) are explicitly modeled in the computational grid, then modeling accuracy is improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent extracts the essential functional characteristic of the Purkinje system (high electrical conductivity) from its complex anatomical structure, and represents it through a simplified distance-based parameterization. Instead of modeling individual Purkinje fibers, the system identifies regions near the endocardium (within distance d) and assigns elevated diffusion coefficients to those regions, capturing the net effect of the conduction system without its structural complexity.
Solution Approach 2:
The patent applies local quality by assigning different electrical diffusion coefficients to different spatial regions based on their distance from the endocardium. Grid cells within distance d from the endocardium receive a first diffusion coefficient (modeling fast conduction), while other regions receive a second, lower diffusion coefficient. This creates spatially varying tissue properties that reflect the localized presence of the Purkinje system.
2Measurement precision
If fine spatial resolution grid is used to model electrical conduction system, then accuracy is improved, but real-time simulation capability is lost due to increased computational cost
Solution Approach 1:
The patent changes the parameter representation from explicit anatomical geometry to a distance-based parameter field. By defining tissue properties as a function of distance from the endocardium (parameter d), the system achieves sub-grid accuracy without requiring fine spatial resolution. This parameter transformation allows coarse grids to capture the essential physics of fast conduction regions, enabling real-time simulation performance.
3Measurement precision
If complex patient-specific anatomical models are created from medical images, then patient-specific accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-processing the medical images to generate a distance map (parameter d) that encodes the spatial relationship between grid cells and the endocardium. This distance field is computed once during model setup and then reused throughout the simulation to determine tissue properties. This preliminary computation avoids repeated complex geometric calculations during real-time simulation, significantly reducing processing time while maintaining patient-specific accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate, real-time patient-specific cardiac electrophysiology simulations, improving intervention planning and guidance while reducing procedure duration and patient stress, by effectively modeling the high-speed conducting tissues like the Purkinje system with sub-grid resolution accuracy.
Implementation Method 1
The method employs the Lattice Boltzmann method for Electrophysiology (LBM-EP) to simulate cardiac electrophysiology using a coarse grid
Implementation Method 2
assigning high diffusion coefficients to grid cells based on the distance from the endocardium
Data Source
AI summary
A method and system for simulating patient-specific cardiac electrophysiology including the effect of the electrical conduction system of the heart is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. The electrical conduction system of the heart of the patient is modeled by determining electrical diffusivity values of cardiac tissue based on a distance of the cardiac tissue from the endocardium. A distance field from the endocardium surface is calculated with sub-grid accuracy using a nested-level set approach. Cardiac electrophysiology for the patient is simulated using a cardiac electrophysiology model with the electrical diffusivity values determined to model the Purkinje network of the patient.