期刊
COMPUTERS & FLUIDS
卷 142, 期 -, 页码 128-138出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compfluid.2016.05.015
关键词
Coronary flow; Hemodynamics; Multiscale cardiovascular simulation; Data assimilation; Parameter estimation; Lumped boundary circulation models; Uncertainty quantification
资金
- NHLBI NIH HHS [R01 HL123689, R01 HL121754] Funding Source: Medline
- NIBIB NIH HHS [R01 EB018302] Funding Source: Medline
- Direct For Computer & Info Scie & Enginr
- Office of Advanced Cyberinfrastructure (OAC) [1556479] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1508794] Funding Source: National Science Foundation
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. (C) 2016 Elsevier Ltd. All rights reserved.
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