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 the order of 0.5 and 1.0 mm was emulated by introducing uncertainty in the shape mode coefficients. A global variance‐based sensitivity analysis was performed to quantify output uncertainty and to determine relative importance of the shape modes. The first shape mode captured the opening/closing behavior of the valve and uncertainty in this mode coefficient accounted for more than 90% of the output variance. However, sensitivity to shape uncertainty is patient‐specific, and the relative importance of the fourth shape mode coefficient tended to increase with increases in valvular area. These results show that geometric uncertainty in the order of image voxel size may lead to substantial uncertainty in CFD‐computed transvalvular pressure‐drops. Moreover, this illustrates that it is essential to assess the impact of geometric uncertainty on model output, and that this should be thoroughly quantified for applications that wish to use image‐based CFD models.

Generation of synthetic aortic valve stenosis geometries for in silico trials

“…Furthermore, the template used to generate the synthetic geometries is very suitable for finite element, CFD or FSI simulations, since it does not contain any holes or distorted elements. Moreover, its refinement level aligns with, or even surpasses the refinement reported by Hoeijmakers et al 7 As a result, the synthetic geometries are well‐suited for simulations and require minimal pre‐processing steps before integration into a simulation pipeline.…”

“…In silico trials could possibly speed up the clinical introduction of improved TAVI devices. Computational fluid dynamics (CFD) and fluid–structure interaction (FSI) models are typically used to simulate aortic valve dynamics before, 7,8 and after treatment 9–13 . Furthermore, finite element methods can be used to simulate contact forces between the aortic root and the TAVI valve during deployment 14–16 .…”

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

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A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach

“… 2020 ; Hoeijmakers et al. 2019 , 2020 , 2021 ). However, this requires advanced meshing procedures since valve geometries must be defined a priori within the boundary of the mesh.…”