Why the study?
An efficient forward solver was needed to handle induced sub-problems in solving inverse problems to quantify parameters in patient-specific left ventricular fluid-structure interaction simulations.
A novel parallel domain decomposition algorithm enables scalable and robust fluid-structure interaction simulations of the left ventricle.
New parallel FSI algorithm enables efficient LV inverse modeling; leaves open clinical validation before parameter quantification.
In this paper, we propose a scalable parallel algorithm for simulating the cardiac fluid-structure interactions (FSI) of a patient-specific human left ventricle. It provides an efficient forward solver to deal with the induced sub-problems in solving an inverse problem that can be used to quantify the interested parameters. The FSI between the blood flow and the myocardium is described in an arbitrary Lagrangian-Eulerian (ALU) framework, in which the velocity and stress are assumed being continuous across the fluid-structure interface. The governing equations are discretized by using a finite element method and a fully implicit backward Eulerian formula, and the resulting algebraic system is solved by using a parallel Newton-Krylov-Schwarz algorithm. We numerically show that the algorithm is robust with respect to multiple model parameters and scales well up to 2300 processor cores. The ability of the proposed method to produce qualitatively true prediction is also demonstrated via comparing the simulation results with the clinic data.
No takes yet. Share an insight, caveat, or question.
Chang et al. (2022) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: