The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls

TL;DR

This study quantifies how input uncertainties affect coronary blood flow and vessel mechanics in patient-specific simulations, highlighting the importance of pressure uncertainties and demonstrating the effectiveness of multi-wavelet stochastic expansion.

Contribution

It introduces a comprehensive uncertainty quantification framework for coronary simulations with deformable walls, comparing multiple stochastic methods for accuracy and efficiency.

Findings

Uncertainty in inlet and intramyocardial pressures significantly impacts simulation outputs.

Elastic modulus uncertainty mainly affects vessel wall mechanics.

Multi-wavelet stochastic expansion outperforms other methods in accuracy and convergence.

Abstract

Cardiovascular simulations are increasingly used for non-invasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an ALE framework for FSI, with a prescribed inlet pressure and open-loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra-coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi MC, stochastic collocation, and multiwavelet stochastic expansion. Variabilities in QoI, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods. From the simulation results, we conclude that the multi-wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.