Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters

TL;DR

This paper presents a machine learning-based reduced order modeling framework for patient-specific coronary artery bypass graft hemodynamics, integrating physical and geometrical parameters to efficiently simulate complex blood flow scenarios.

Contribution

It introduces a novel ROM approach combining finite volume discretization, Proper Orthogonal Decomposition, and neural networks for patient-specific coronary flow analysis.

Findings

Achieves significant computational speedup during online simulations.

Maintains acceptable accuracy compared to full order models.

Incorporates mesh deformation via Free Form Deformation for patient-specific geometries.

Abstract

In this work the development of a machine learning-based Reduced Order Model (ROM) for the investigation of hemodynamics in a patient-specific configuration of Coronary Artery Bypass Graft (CABG) is proposed. The computational domain is referred to left branches of coronary arteries when a stenosis of the Left Main Coronary Artery (LMCA) occurs. The method extracts a reduced basis space from a collection of high-fidelity solutions via a Proper Orthogonal Decomposition (POD) algorithm and employs Artificial Neural Networks (ANNs) for the computation of the modal coefficients. The Full Order Model (FOM) is represented by the incompressible Navier-Stokes equations discretized using a Finite Volume (FV) technique. Both physical and geometrical parametrization are taken into account, the former one related to the inlet flow rate and the latter one related to the stenosis severity. With respect to the previous works focused on the development of a ROM framework for the evaluation of coronary artery disease, the novelties of our study include the use of the FV method in a patient-specific configuration, the use of a data-driven ROM technique and the mesh deformation strategy based on a Free Form Deformation (FFD) technique. The performance of our ROM approach is analyzed in terms of the error between full order and reduced order solutions as well as the speedup achieved at the online stage.