Cerebral Aneurysm Flow Diverter Modeled as a Thin Inhomogeneous Porous Medium in Hemodynamic Simulations

TL;DR

This paper introduces a novel inhomogeneous porous medium model for simulating cerebral aneurysm flow post-stenting, significantly reducing computational cost while maintaining high accuracy, thus aiding clinical decision-making.

Contribution

The study proposes a new inhomogeneous porous medium approach that models flow diverters more accurately and efficiently than traditional homogeneous models in patient-specific aneurysm simulations.

Findings

iPM runs 500% faster than explicit CFD simulations.

Achieves 94%-99% accuracy compared to explicit CFD.

Applicable to patient-specific aneurysm cases.

Abstract

Rapid and accurate simulation of cerebral aneurysm flow modifications by flow diverters (FDs) can help improving patient-specific intervention and predicting treatment outcome. However, with explicit FD devices being placed in patient-specific aneurysm model, the computational domain must be resolved around the thin stent wires, leading to high computational cost in computational fluid dynamics (CFD). Classic homogeneous porous medium (PM) methods cannot accurately predict the post-stenting aneurysmal flow field due to the inhomogeneous FD wire distributions on anatomic arteries. We propose a novel approach that models the FD flow modification as a thin inhomogeneous porous medium (iPM). It improves over classic PM approaches in that, first, FD is treated as a screen, which is more accurate than the classic Darcy-Forchheimer relation based on 3D PM. second, the pressure drop is calculated using local FD geometric parameters across an inhomogeneous PM, which is more realistic. To test its accuracy and speed, we applied the iPM technique to simulate the post stenting flow field in three patient-specific aneurysms and compared the results against CFD simulations with explicit FD devices. The iPM CFD ran 500% faster than the explicit CFD while achieving 94%-99% accuracy. Thus iPM is a promising clinical bedside modeling tool to assist endovascular interventions with FD and stents.