Tran Justin S, Schiavazzi Daniele E, Ramachandra Abhay B, Kahn Andrew M, Marsden Alison L
Department of Pediatrics (Cardiology), Bioengineering and ICME, Stanford University, Stanford, CA, USA.
Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Comput Fluids. 2017 Jan 5;142:128-138. doi: 10.1016/j.compfluid.2016.05.015. Epub 2016 May 16.
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.
动脉粥样硬化性冠状动脉疾病可导致冠状动脉狭窄、急性冠状动脉阻塞，并最终引发心肌梗死，是全球发病和死亡的主要原因。冠状动脉血流的非侵入性特征对于增进对该疾病的理解、预防和治疗至关重要。现在，计算模拟仅使用非侵入性测量就能生成临床相关的血流动力学量，在多尺度框架中将详细的三维流体力学与生理模型相结合。然而，这些模型需要指定大量输入参数，并且通常是手动调整，而没有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。我们提出了一种基于自适应马尔可夫链蒙特卡罗采样的自动贝叶斯参数估计方法，该方法吸收了常规临床护理中常见的非侵入性量，量化估计参数中的不确定性，并计算对局部预测血流动力学指标的置信度。
