Numerical Modeling of Pulse Wave Propagation in a Stenosed Artery using Two-Way Coupled Fluid Structure Interaction (FSI)

TL;DR

This study uses two-way coupled fluid-structure interaction modeling to analyze pulse wave propagation in stenosed arteries, revealing how stenosis affects wave damping and wall displacements, aiding non-invasive cardiovascular diagnostics.

Contribution

It introduces a validated numerical FSI model for simulating pulse wave behavior in stenosed arteries, enhancing understanding of PWV changes due to stenosis.

Findings

Stenosis causes damping of pulse waves.

Flow instabilities generate high wall displacements downstream.

PWV analysis can distinguish healthy and stenosed arteries.

Abstract

As the heart beats, it creates fluctuation in blood pressure leading to a pulse wave that propagates by displacing the arterial wall. These waves travel through the arterial tree and carry information about the medium that they propagate through as well as information of the geometry of the arterial tree. Pulse wave velocity (PWV) can be used as a non-invasive diagnostic tool to study the functioning of cardiovascular system. A stenosis in an artery can dampen the pulse wave leading to changes in the propagating pulse. Hence, PWV analysis can be performed to detect a stenosed region in arteries. This paper presents a numerical study of pulse wave propagation in a stenosed artery by means of two-way coupled fluid structure interaction (FSI). The computational model was validated by the comparison of the simulated PWV results with theoretical values for a healthy artery. Propagation of the pulse waves in the stenosed artery was compared with healthy case using spatiotemporal maps of wall displacements. The analysis for PWV showed significance differences between the healthy and stenosed arteries including damping of propagating waves and generation of high wall displacements downstream the stenosis caused by flow instabilities. This approach can be used to develop patient-specific models that are capable of predicting PWV signatures associated with stenosis changes. The knowledge gained from these models may increase utility of this approach for managing patients at risk of stenosis occurrence.