Numerical models are increasingly used for non-invasive diagnosis and treatment planning in coronary artery disease, where service-based technologies have proven successful in identifying hemodynamically significant and hence potentially dangerous vascular anomalies. Despite recent progress towards clinical adoption, many results in the field are still based on a deterministic characterization of blood flow, with no quantitative assessment of the variability of simulation outputs due to uncertainty from multiple sources. In this study, we focus on parameters that are essential to construct accurate patient-specific representations of the coronary circulation, such as aortic pressure waveform, intramyocardial pressure and quantify how their uncertainty affects clinically relevant model outputs. We construct a deformable model of the left coronary artery subject to a prescribed inlet pressure and with open-loop outlet boundary conditions, treating fluid-structure interaction through an Arbitrary-Lagrangian-Eulerian frame of reference. Random input uncertainty is estimated directly from repeated clinical measurements from intra-coronary catheterization and complemented by literature data. We also achieve significant computational cost reductions in uncertainty propagation thanks to multifidelity Monte Carlo estimators of the outputs of interest, leveraging the ability to generate, at practically no cost, one- and zero-dimensional low-fidelity representations of left coronary artery flow, with appropriate boundary conditions. The results demonstrate how the use of multi-fidelity control variate estimators leads to significant reductions in variance and accuracy improvements with respect to traditional Monte-Carlo.

中文翻译：

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临床数据不确定下冠状动脉循环模型的多保真估计器

数值模型越来越多地用于冠状动脉疾病的非侵入性诊断和治疗计划，其中基于服务的技术已被证明可以成功识别具有血流动力学意义并因此具有潜在危险的血管异常。尽管最近在临床采用方面取得了进展，但该领域的许多结果仍然基于血流的确定性表征，由于来自多个来源的不确定性，没有对模拟输出的可变性进行定量评估。在这项研究中，我们专注于构建冠状动脉循环的准确患者特异性表征所必需的参数，例如主动脉压力波形、心肌内压力，并量化它们的不确定性如何影响临床相关模型输出。我们构建了受规定入口压力和开环出口边界条件影响的左冠状动脉的可变形模型，通过任意-拉格朗日-欧拉参考系处理流体-结构相互作用。随机输入的不确定性是直接从冠状动脉内导管插入术的重复临床测量中估计出来的，并辅以文献数据。由于感兴趣输出的多保真蒙特卡罗估计器，我们还实现了不确定性传播的显着计算成本降低，利用几乎免费生成左冠状动脉血流的一维和零维低保真表示的能力，适当的边界条件。