An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Computational models of atrial electrophysiology (EP) are increasingly
utilized for applications such as the development of advanced mapping systems,
personalized clinical therapy planning, and the generation of virtual cohorts
and digital twins. These models have the potential to establish robust causal
links between simulated in silico behaviors and observed human atrial EP,
enabling safer, cost-effective, and comprehensive exploration of atrial
dynamics. However, current state-of-the-art approaches lack the fidelity and
scalability required for regulatory-grade applications, particularly in
creating high-quality virtual cohorts or patient-specific digital twins.
Challenges include anatomically accurate model generation, calibration to
sparse and uncertain clinical data, and computational efficiency within a
streamlined workflow. This study addresses these limitations by introducing
novel methodologies integrated into an automated end-to-end workflow for
generating high-fidelity digital twin snapshots and virtual cohorts of atrial
EP. These innovations include: (i) automated multi-scale generation of
volumetric biatrial models with detailed anatomical structures and fiber
architecture; (ii) a robust method for defining space-varying atrial parameter
fields; (iii) a parametric approach for modeling inter-atrial conduction
pathways; and (iv) an efficient forward EP model for high-fidelity
electrocardiogram computation. We evaluated this workflow on a cohort of 50
atrial fibrillation patients, producing high-quality meshes suitable for
reaction-eikonal and reaction-diffusion models and demonstrating the ability to
simulate atrial ECGs under parametrically controlled conditions. These
advancements represent a critical step toward scalable, precise, and clinically
applicable digital twin models and virtual cohorts, enabling enhanced
patient-specific predictions and therapeutic planning.
