Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations

Abstract

Atherosclerotic coronary artery disease, which can result in coronary artery stenosis, acute coronary artery occlusion, and eventually myocardial infarction, is a major cause of morbidity and mortality worldwide. Non-invasive characterization of coronary blood flow is important to improve understanding, prevention, and treatment of this disease. Computational simulations can now produce clinically relevant hemodynamic quantities using only non-invasive measurements, combining detailed three dimensional fluid mechanics with physiological models in a multiscale framework. These models, however, require specification of numerous input parameters and are typically tuned manually without accounting for uncertainty in the clinical data, hindering their application to large clinical studies. We propose an automatic, Bayesian, approach to parameter estimation based on adaptive Markov chain Monte Carlo sampling that assimilates non-invasive quantities commonly acquired in routine clinical care, quantifies the uncertainty in the estimated parameters and computes the confidence in local predicted hemodynamic indicators.

Patient-Specific Simulations Reveal Significant Differences in Mechanical Stimuli in Venous and Arterial Coronary Grafts

“…Current efforts are directed towards development of mechanics-based models of vascular growth and remodeling [40]. Recent efforts have also been directed towards systematic parameter estimation and uncertainty quantification, which could streamline the tuning process in CABG simulations [42]. Third, while the boundary conditions were tuned to patient-specific data, material properties and blood viscosity were not patient specific.…”

A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling

“…We are therefore interested in first determining the distributions of boundary conditions by performing data assimilation, i.e., by estimating the 0D model parameters using adaptive Markov chain Monte Carlo (MCMC). Due to the number of model evaluations typically required to produce independent samples from a stationary posterior distribution in MCMC, a condensed resistive representation of the three-dimensional model is also computed, leading to a dramatic reduction in the overall computational cost (from several hours to a fraction of a second, see [33], for a single model solution). The proposed multi-resolution approach to uncertainty propagation is employed to propagate the distributions of assimilated boundary conditions through a full multi-scale model, therefore quantifying the variability in local hemodynamic indicators of interest.…”

“…Specifically, we discuss clinical target selection for data assimilation and preliminary identifiability analysis. Please refer to [33] for further details. Targets were acquired using routine clinical measurements, population average values, echocardiography data, and complemented with literature data on coronary flow.…”

Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta

“…The subject is part of the data-set analyzed in Morbiducci et al (2011a). models: reconstructed vessel geometry (Sankaran & Marsden 2011;Sankaran et al 2015Sankaran et al , 2016, input and output BCs (Sankaran & Marsden 2011;Morbiducci et al 2013;Tiago et al 2014;Valen-Sendstad et al 2015;Schiavazzi et al 2016;Tran et al 2017), vessel distensibility and motion (Jin et al 2003;Zhao et al 2000;Eck et al 2016;Javadzadegan et al 2016) and rheological properties of blood (Lee & Steinman 2007;Morbiducci et al 2011b). In a recent study, the Authors reported a numerical experiment in which different possible strategies of applying PC-MRI measured flow data as BCs in computational hemodynamic models of healthy human aorta were implemented (Morbiducci et al 2013).…”

“…Different numerical approaches have been used to quantify uncertainty propagation in blood flow simulations, starting from sampling techniques like Monte Carlo method (Huberts et al 2012;Tran et al 2017) to more compact projection-based methods as the polynomial chaos expansions (Sankaran & Marsden 2011;Quicken et al 2016;Eck et al 2017). A comparison between the two approaches has been presented by Eck et al (2016), who showed that polynomial chaos expansions perform better for low dimensional problems, while Monte Carlo method is more suitable for higher dimension problems.…”