Automated Tuning for Parameter Identification and Uncertainty Quantification in Multi-scale Coronary Simulations.
Automated Tuning for Parameter Identification and Uncertainty Quantification in Multi-scale Coronary Simulations.
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DOI：
10.1016/j.compfluid.2016.05.015
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发表时间：
2017-01-05
影响因子：
2.8
通讯作者：
Marsden AL
中科院分区：
文献类型：
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作者：
Tran JS;Schiavazzi DE;Ramachandra AB;Kahn AM;Marsden AL
可导致冠状动脉狭窄、急性冠状动脉闭塞并最终导致心肌梗死的动脉粥样硬化性冠状动脉疾病是全球发病率和死亡率的主要原因。冠状动脉血流的非侵入性表征对于提高对这种疾病的理解、预防和治疗非常重要。计算模拟现在可以仅使用非侵入性测量产生临床相关的血液动力学量，将详细的三维流体力学与多尺度框架中的生理模型相结合。然而，这些模型需要指定许多输入参数，并且通常手动调整而不考虑临床数据中的不确定性，这阻碍了它们在大型临床研究中的应用。我们提出了一种自动的，贝叶斯的，基于自适应马尔可夫链蒙特卡罗采样的参数估计方法，该方法吸收了常规临床护理中常用的非侵入性量，量化了估计参数的不确定性，并计算了局部预测血流动力学指标的置信度。
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.