临床导出的参数数据不确定性对具有可变形壁的患者特定冠状动脉模拟的影响
The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls
摘要：
心血管模拟越来越多地用于心血管疾病的非侵入性诊断，以指导治疗决策以及医疗设备的设计。由于输入的不确定性，模拟输出的可变性的定量评估是进一步整合心血管模拟在临床工作流程中的关键一步。在这项研究中，我们提出了不确定性量化的计算模型的冠状动脉循环，调查的影响，不确定性参数，包括冠状动脉压力波形，心肌内压力，形态指数，血管壁杨氏模量。我们采用了左冠状动脉模型与可变形的血管壁，模拟通过ALE框架FSI，具有规定的入口压力和开环集总参数网络出口边界条件。不确定输入的随机建模是根据冠状动脉内导管插入术数据或从文献中收集的数据确定的。不确定性传播使用几种方法，包括蒙特卡罗，准MC，随机配置，和多小波随机扩展。QoI的变化，包括分支压力，流量，壁剪切应力，和壁变形进行了评估。我们发现，入口压力和心肌内压力的不确定性显着影响所有由此产生的生活质量，而弹性模量的不确定性只影响血管壁的机械响应。形态指数的变异性对冠状动脉血流动力学或壁力学的影响很小。最后，我们比较了使用几种不确定性传播方法的QOI统计的收敛行为。从模拟结果中，我们得出结论，多小波随机展开显示出优越的精度和性能，拟蒙特卡罗和随机配置方法。
英文摘要：
Cardiovascular simulations are increasingly used for non-invasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an ALE framework for FSI, with a prescribed inlet pressure and open-loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra-coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi MC, stochastic collocation, and multiwavelet stochastic expansion. Variabilities in QoI, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods. From the simulation results, we conclude that the multi-wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.
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