# Computational Tools for Cardiac Simulation -- GPU-Parallel Multiphysics

## TL;DR

This paper introduces a GPU-accelerated computational approach for simulating the entire human heartbeat, integrating electrophysiology, mechanics, and fluid dynamics, enabling rapid and cost-effective cardiac modeling.

## Contribution

It reformulates key continuum mechanics models for parallel processing and demonstrates a GPU-based method capable of simulating a full heartbeat in minutes.

## Key findings

- Complete heartbeat simulation on a single GPU within minutes
- Reformulation of continuum mechanics models for GPU parallelization
- OpenCL implementation independent of third-party libraries

## Abstract

Cardiovascular disease affects millions of people worldwide and its social and economic cost clearly motivates scientific research. Computer simulation can lead to a better understanding of cardiac physiology, and for pathology presents opportunities for low-cost and low-risk design and testing of therapies, including surgical and pharmacological intervention as well as automated diagnosis and screening. Currently, the simulation of a whole heart model, including the interaction of electrophysiology, solid mechanics and fluid dynamics is the subject of ongoing research in computational science. Typically, the computation of a single heartbeat requires many processor hours on a supercomputer. The financial and ultimately environmental cost of such a computation prevents it from becoming a viable clinical or research solution. We re-formulate the standard mathematical models of continuum mechanics, such as the Bidomain Model, Finite Strain Theory and the Navier-Stokes Equations, specifically for parallel processing and show proof-of-concept of a computational approach that can generate a complete description of a human heartbeat on a single Graphics Processing Unit (GPU) within a few minutes. The approach is based on a Finite Volume Method (FVM) discretisation which is both matrix- and mesh-free, ideally suited to voxel-based medical imaging data. The solution of nonlinear ordinary and partial differential equations proceeds via the method of lines and operator-splitting. The resulting algorithm is implemented in the OpenCL standard and can run on almost any platform. It does not perform any CPU processing and has no dependence on third-party software libraries.

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Source: https://tomesphere.com/paper/2302.12519