Parametric model order reduction and its application to inverse analysis of large nonlinear coupled cardiac problems

TL;DR

This paper introduces a parametric model order reduction technique for large nonlinear coupled cardiac models, significantly speeding up simulations and enabling efficient patient-specific inverse analysis for clinical applications.

Contribution

It proposes a novel projection-based reduction method tailored for coupled cardiac systems, incorporating parameter changes via subspace interpolation and demonstrating integration into inverse analysis workflows.

Findings

Achieves significant speedups in cardiac simulations.

Maintains accurate key outputs with few reduced degrees of freedom.

Successfully integrates into gradient-based optimization for inverse problems.

Abstract

Predictive high-fidelity finite element simulations of human cardiac mechanics co\-mmon\-ly require a large number of structural degrees of freedom. Additionally, these models are often coupled with lumped-parameter models of hemodynamics. High computational demands, however, slow down model calibration and therefore limit the use of cardiac simulations in clinical practice. As cardiac models rely on several patient-specific parameters, just one solution corresponding to one specific parameter set does not at all meet clinical demands. Moreover, while solving the nonlinear problem, 90\% of the computation time is spent solving linear systems of equations. We propose a novel approach to reduce only the structural dimension of the monolithically coupled structure-windkessel system by projection onto a lower-dimensional subspace. We obtain a good approximation of the displacement field as well as of key scalar cardiac outputs even with very few reduced degrees of freedom while achieving considerable speedups. For subspace generation, we use proper orthogonal decomposition of displacement snapshots. To incorporate changes in the parameter set into our reduced order model, we provide a comparison of subspace interpolation methods. We further show how projection-based model order reduction can be easily integrated into a gradient-based optimization and demonstrate its performance in a real-world multivariate inverse analysis scenario. Using the presented projection-based model order reduction approach can significantly speed up model personalization and could be used for many-query tasks in a clinical setting.