Statistical Atlas-Based Surrogate Model of Biventricular Wall Mechanics

Abstract

Here we use a statistical atlas of end-diastolic (ED) and end-systolic (ES) biventricular shapes - previously derived from the UK Biobank imaging substudy - to generate meshes for finite element (FE) simulations of ventricular wall mechanics. The models used the Holzapfel-Ogden constitutive law for passive material properties and a time-varying elastance model of systolic tension development. Simulated ED and ES deformations were projected onto the shape atlas and the principal components were used to train a multi-layer perceptron as a surrogate model. The input layer included shape modes of the unloaded ventricular geometry, and material parameters and ventricular pressures at ED and ES. After training with 444 simulations, the surrogate model achieved a mean square error in predicted displacements of 97%, demonstrating good fidelity to the simulated ground truth. This approach may enable accurate prediction of ventricular wall mechanics without computationally expensive finite element analysis, offering a more feasible method for rapid, subject-specific cardiac modeling.