The impact of shape uncertainty on aortic-valve pressure-drop computations.

ABSTRACT: Patient-specific image-based computational fluid dynamics (CFD) is widely adopted in the cardiovascular research community to study hemodynamics, and will become increasingly important for personalized medicine. However, segmentation of the flow domain is not exact and geometric uncertainty can be expected which propagates through the computational model, leading to uncertainty in model output. Seventy-four aortic-valves were segmented from computed tomography images at peak systole. Statistical shape modeling was used to obtain an approximate parameterization of the original segmentations. This parameterization was used to train a meta-model that related the first five shape mode coefficients and flowrate to the CFD-computed transvalvular pressure-drop. Consequently, shape uncertainty in

SUBMITTER: Hoeijmakers MJMM 

PROVIDER: S-EPMC9286381 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature