Advancing aortic stenosis assessment: validation of fluid-structure interaction models against 4D flow MRI data

TL;DR

This study validates patient-specific fluid-structure interaction models of the aortic valve against 4D flow MRI data, demonstrating their potential to improve diagnosis and device design in clinical settings.

Contribution

It introduces a rigorous validation framework for computational aortic valve models using in vivo 4D flow MRI data, advancing clinical application readiness.

Findings

Models accurately replicate flow dynamics within uncertainty ranges.

Computational models help reduce noise in in vivo data.

Framework supports integration of in silico tools into clinical practice.

Abstract

Systematic in vivo validations of computational models of the aortic valve remain scarce, despite successful validation against in vitro data. Utilizing a combination of computed tomography and 4D flow magnetic resonance imaging data, we developed patient-specific fluid-structure interaction models of the aortic valve immersed in the aorta for five patients in the pre-transcatheter aortic valve replacement configuration. Incorporating also an in vitro setup of the valve, our computational models are subjected to rigorous validation against 4D flow measurements. Our results demonstrate the models' capacity to accurately replicate flow dynamics within established ranges of uncertainties mainly arising from 4D flow noise. In addition, we illustrate how computational models can serve as valuable cross-checks to reduce noise and erratic behaviour of in vivo data. This study represents a significant step towards integrating in silico technologies into real clinical contexts, providing a robust framework for improving aortic stenosis diagnosis and the design of next-generation aortic valve bioprostheses.