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

Abstract

Cardiovascular simulations are increasingly used for noninvasive 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 Arbitrary‐Lagrangian‐Eulerian framework for fluid‐structure interaction, 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 Monte Carlo sampling, stochastic collocation, and multi‐wavelet stochastic expansion. Variabilities in the quantities of interest, 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 used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. 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.

Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks

“…Combined uncertainty in vessel wall material properties and hemodynamics are investigated in [40] for several patient-specific models of coronary artery bypass grafting, leveraging a novel submodeling approach to focus the analysis only on venous and arterial bypass grafts. This and other studies focusing on the coronary circulation, (see, e.g., [33]) contributed to show a loose coupling between hemodynamics and wall mechanics for such anatomies. One dimensional models have been used to better understand main pulmonary artery pressure uncertainty in mice due to material property and image segmentation uncertainty [3,4].…”

Integrating multi-fidelity blood flow data with reduced-order data assimilation

“…To approximate the covariance matrix, we have proposed a very simple method based on a very limited number of steady simulations. This simple approach was selected to reduce the high computational cost involved in rigorous uncertainty quantification with Monte Carlo type methods [56,60], which are also used in the ensemble Kalman filter approach. However, it should be noted that even with more rigorous uncertainty quantification, the Kalman filter framework is assuming that the error distribution is Gaussian, which is likely not the case in practice.…”

Solution adaptive refinement of cut‐cell Cartesian meshes can improve FDA nozzle computational fluid dynamics efficiency

“…Cardiovascular flows are often transitional 12‐14 and the most accurate CFD approaches, such as direct numerical simulation (DNS) and large eddy simulation (LES), require large computing resources 13,15‐21 . For maximum impact CFD simulations must be computationally efficient enough to be used in parametric design studies, 22,23 large patient population studies, generating machine learning data and robust uncertainty quantification 24,25 . To be an effective tool in studying cardiovascular disease and designing new treatments CFD needs to move from the realm of “high performance” to “high throughput” computing where simulations are efficient enough that large numbers of simulations be performed simultaneously.…”