Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations. (5th January 2017)

• Record Type: Journal Article
• Authors: Tran, Justin S.; Schiavazzi, Daniele E.; Ramachandra, Abhay B.; Kahn, Andrew M.; Marsden, Alison L.
• Is Part Of: Computers & fluids. Volume 142(2017)
• Page Start: 128
• Page End: 138
• Publication Date: 2017-01-05
• Subjects: Coronary flow -- Hemodynamics -- Multiscale cardiovascular simulation -- Data assimilation -- Parameter estimation -- Lumped boundary circulation models -- Uncertainty quantification; 00–01 -- 99–00; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2016.05.015
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: A framework is presented for automated tuning of lumped parameter network boundary conditions in cardiovascular simulations. An automated Bayesian approach is employed, achieving excellent agreement with clinical targets. The method is demonstrated using patient specific data for six coronary bypass patients and one normal. Uncertainties are propagated to 3D simulation results to demonstrate success of the method in a multi-scale framework. 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 multi-scale 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.