An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular simulations

TL;DR

This paper introduces an optimal control method to automatically estimate resistance-type boundary conditions in cardiovascular CFD simulations, improving accuracy using patient-specific data.

Contribution

It presents a novel automated framework for boundary condition estimation in cardiovascular simulations using optimal control, outperforming traditional manual tuning methods.

Findings

More accurate data assimilation than Murray's law and Ohm's law methods

Effective estimation of boundary conditions from 4D-Flow MRI data

Demonstrated on four patient-specific aortic arches

Abstract

The choice of appropriate boundary conditions is a fundamental step in computational fluid dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact, highly affect the computed pressure and flow rates, and consequently haemodynamic indicators such as wall shear stress, which are of clinical interest. Devising automated procedures for the selection of boundary conditions is vital to achieve repeatable simulations. However, the most common techniques do not automatically assimilate patient-specific data, relying instead on expensive and time-consuming manual tuning procedures. In this work, we propose a technique for the automated estimation of outlet boundary conditions based on optimal control. The values of resistive boundary conditions are set as control variables and optimized to match available patient-specific data. Experimental results on four aortic arches demonstrate that the proposed framework can assimilate 4D-Flow MRI data more accurately than two other common techniques based on Murray's law and Ohm's law.