In silico modeling for personalized stenting in aortic coarctation

TL;DR

This paper presents a computational framework combining CFD and MRI data to personalize stent interventions for aortic coarctation, aiming to optimize treatment by predicting post-stent blood flow and geometry.

Contribution

It introduces an in silico modeling approach integrating LBM-based CFD and image data to predict personalized stent outcomes in CoA patients.

Findings

LBM-based LES accurately models aortic flow with acceptable computational cost.

The framework can predict post-stent geometry and blood flow effectively.

Optimal stent selection can be guided by pressure and shear stress analysis.

Abstract

Stent intervention is a recommended therapy to reduce the pressure gradient and restore blood flow for patients with coarctation of the aorta (CoA). In this work, we developed a framework for personalized stent intervention in CoA using in silico modeling, combining computational fluid dynamics (CFD) and image-based prediction of the geometry of the aorta after stent intervention. Firstly, the blood flow in the aorta, whose geometry was reconstructed from magnetic resonance imaging (MRI) data, was numerically modeled using the lattice Boltzmann method (LBM). Both large eddy simulation (LES) and direct numerical simulation (DNS) were considered to adequately resolve the turbulent hemodynamics, with boundary conditions extracted from phase-contrast flow MRI. By comparing the results from CFD and 4D-Flow MRI in 3D-printed flow phantoms, we concluded that the LBM based LES is capable of obtaining accurate aortic flow with acceptable computational cost. In silico stent implantation for a patient with CoA was then performed by predicting the deformed geometry after stent intervention and predicting the blood flow. By evaluating the pressure drop and maximum wall shear stress, an optimal stent can be selected.