Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling
Dongwei Ye, Pavel Zun, Valeria Krzhizhanovskaya, Alfons G. Hoekstra

TL;DR
This paper develops a surrogate Gaussian process model to quantify uncertainty in a 3D in-stent restenosis model, revealing key parameters influencing early and late-stage neointimal growth.
Contribution
It introduces a surrogate modeling approach using Gaussian processes with proper orthogonal decomposition for efficient uncertainty quantification of a complex restenosis model.
Findings
Uncertainty in fenestration affects initial neointimal growth.
Blood flow velocity and regeneration time influence later-stage uncertainty.
Parameter threshold strain has a minor impact on uncertainty.
Abstract
In-Stent Restenosis is a recurrence of coronary artery narrowing due to vascular injury caused by balloon dilation and stent placement. It may lead to the relapse of angina symptoms or to an acute coronary syndrome. An uncertainty quantification of a model for In-Stent Restenosis with four uncertain parameters (endothelium regeneration time, the threshold strain for smooth muscle cells bond breaking, blood flow velocity and the percentage of fenestration in the internal elastic lamina) is presented. Two quantities of interest were studied, namely the average cross-sectional area and the maximum relative area loss in a vessel. Due to the computational intensity of the model and the number of evaluations required in the uncertainty quantification, a surrogate model, based on Gaussian process regression with proper orthogonal decomposition, was developed which subsequently replaced the…
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Taxonomy
TopicsCoronary Interventions and Diagnostics · Cardiac Imaging and Diagnostics
MethodsGaussian Process
