  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.
