Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks

TL;DR

This paper introduces a physics-informed neural network framework that non-invasively reconstructs three-dimensional cardiac activation patterns and mechanical behaviors from deformation data, integrating physical laws with machine learning for improved cardiac diagnostics.

Contribution

The study develops a novel physics-informed neural network that combines mechanistic cardiac models with data-driven inference to accurately reconstruct activation dynamics from limited measurements.

Findings

Accurately reconstructs activation patterns under noise and low resolution.

Preserves global propagation and activation timing.

Demonstrates potential for patient-specific cardiac assessment.

Abstract

Cardiac arrhythmogenesis is governed by complex electromechanical interactions that are not directly observable in vivo, motivating the development of non-invasive computational approaches for reconstructing three-dimensional activation dynamics. We present a physics-informed neural network framework for recovering cardiac activation patterns, active tension propagation, deformation fields, and hydrostatic pressure from measurable deformation data in simplified left ventricular geometries. Our approach integrates nonlinear anisotropic constitutive modeling, heterogeneous fiber orientation, weak formulations of the governing mechanics, and finite-element-based loss functions to embed physical constraints directly into training. We demonstrate that the proposed framework accurately reconstructs spatiotemporal activation dynamics under varying levels of measurement noise and reduced spatial resolution, while preserving global propagation patterns and activation timing. By coupling mechanistic modeling with data-driven inference, this method establishes a pathway toward patient-specific, non-invasive reconstruction of cardiac activation, with potential applications in digital phenotyping and computational support for arrhythmia assessment.