Abstract
Numerical models are increasingly used for noninvasive diagnosis and treatment planning in coronary artery disease, where service-based technologies have proven successful in identifying hemodynamically significant and hence potentially dangerous vascular anomalies. Despite recent progress towards clinical adoption, many results in the field are still based on a deterministic characterization of blood flow, with no quantitative assessment of the variability of simulation outputs due to uncertainty from multiple sources. In this study, we focus on parameters that are essential to construct accurate patient-specific representations of the coronary circulation, such as aortic pressure waveform and intramyocardial pressure, and quantify how their uncertainty affects clinically relevant model outputs. We construct a deformable model of the left coronary artery subject to a prescribed inlet pressure and with open-loop outlet boundary conditions, treating fluid-structure interaction through an arbitrary-Lagrangian-Eulerian framework. Random input uncertainty is estimated directly from repeated clinical measurements from intracoronary catheterization and complemented by literature data. We also achieve significant computational cost reductions in uncertainty propagation thanks to multifidelity Monte Carlo estimators of the outputs of interest, leveraging the ability to generate, at practically no cost, one- and zero-dimensional low-fidelity representations of left coronary artery flow, with appropriate boundary conditions. The results demonstrate how the use of multifidelity control variate estimators leads to significant reductions in variance and accuracy improvements with respect to traditional Monte Carlo. In particular, the combination of three-dimensional hemodynamics simulations and zero-dimensional lumped parameter network models produces the best results, with only a negligible (less than 1%) computational overhead.