Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling

Ye, Dongwei; Zun, Pavel; Krzhizhanovskaya, Valeria V.; Hoekstra, Alfons G.

doi:10.1098/rsif.2021.0864

Cited by 17 publications

(3 citation statements)

References 58 publications

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“…However, owing to the complexity and scale of cardiovascular flow problems, the evaluation of the model could be computationally expensive, especially in the cases where a large number of evaluations are required, such as design optimisation, reliability analysis and uncertainty quantification [17,18,19]. Reduced order modelling (ROM) provides a high-fidelity framework to reduce the computational complexity for solving parametric PDEs [18,20].…”

Section: Introductionmentioning

confidence: 99%

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

Ye¹,

Krzhizhanovskaya²,

Hoekstra³

2023

Preprint

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Computational fluid dynamics is a common tool in cardiovascular science and engineering to simulate, predict and study hemodynamics in arteries. However, owing to the complexity and scale of cardiovascular flow problems, the evaluation of the model could be computationally expensive, especially in those cases where a large number of evaluations are required, such as uncertainty quantification and design optimisation. In such scenarios, the model may have to be repeatedly evaluated due to the changes or distinctions of simulation domains. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages surface registration to parameterise those similar but distinct shapes and formulate corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphism constructed between the reference domain and target domain. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on reduced coefficients of geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios.

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Section: Introductionmentioning

confidence: 99%

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

Ye¹,

Krzhizhanovskaya²,

Hoekstra³

2023

Preprint

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“…If the digital twin has to provide results in real time, then some form of reduced-order modeling is critical. Projection-based reduced-order models (ROMs) for the human heart has been an active field of research in the past few years [3,27,42,48,65]. Of particular note is the recent work of Quarteroni and collaborators [25,26,51,53] which has resulted in significant advance towards using surrogate or reduced models to accelerate the simulations of a variety of physics involved in the heart functioning.…”

Section: Introductionmentioning

confidence: 99%

Fast and reliable reduced‐order models for cardiac electrophysiology

Chellappa,

Cansız,

Feng

et al. 2023

GAMM-Mitteilungen

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Mathematical models of the human heart increasingly play a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The ultimate aim is to aid medical practitioners diagnose and treat the many ailments affecting the heart. Towards this, modeling cardiac electrophysiology is crucial as the heart's electrical activity underlies the contraction mechanism and the resulting pumping action. Apart from modeling attempts, the pursuit of efficient, reliable, and fast solution algorithms has been of great importance in this context. The governing equations and the constitutive laws describing the electrical activity in the heart are coupled, nonlinear, and involve a fast moving wave front, which is generally solved by the finite element method. The numerical treatment of this complex system as part of a virtual heart model is challenging due to the necessity of fine spatial and temporal resolution of the domain. Therefore, efficient surrogate models are needed to predict the electrical activity in the heart under varying parameters and inputs much faster than the finely resolved models. In this work, we develop an adaptive, projection‐based reduced‐order surrogate model for cardiac electrophysiology. We introduce an a posteriori error estimator that can accurately and efficiently quantify the accuracy of the surrogate model. Using the error estimator, we systematically update our surrogate model through a greedy search of the parameter space. Furthermore, using the error estimator, the parameter search space is dynamically updated such that the most relevant samples get chosen at every iteration. The proposed adaptive surrogate model is tested on three benchmark models to illustrate its efficiency, accuracy, and ability of generalization.

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“…However, the damage during PCI leads to an excessive amount of neointima formation and a repeat narrowing of the vessel, a condition known as in-stent restenosis (ISR). To study the ISR process, a three-dimensional computational model, ISR3D, has been developed to simulate the process and predict the restenosis progression under different scenarios [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling

Kakhaia¹,

Zun²,

Ye³

et al. 2022

Preprint

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Disorders of coronary arteries lead to severe health problems such as atherosclerosis, angina, heart attack and even death. Considering the clinical significance of coronary arteries, an efficient computer model is a vital step towards tissue engineering, enhancing the research of coronary diseases, and developing medical treatment and interventional tools. In this work, we apply inverse uncertainty quantification to a microscale agent-based arterial tissue model, a component of the 3D multiscale model of in-stent restenosis (ISR3D). IUQ provides calibration of the arterial tissue model to achieve realistic mechanical behaviour in line with the experimental data measured from the tissue's macroscopic behaviour. Bayesian calibration with bias term correction is applied as an IUQ technique to reduce the uncertainty of unknown polynomial coefficients of the attractive force function and achieve agreement with the

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Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling

Cited by 17 publications

References 58 publications

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

Fast and reliable reduced‐order models for cardiac electrophysiology

Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling

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