The choice of appropriate boundary conditions is a fundamental step in computational fluid dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact, highly affect the computed pressure and flow rates, and consequently haemodynamic indicators such as wall shear stress (WSS), which are of clinical interest. Devising automated procedures for the selection of boundary conditions is vital to achieve repeatable simulations. However, the most common techniques do not automatically assimilate patient-specific data, relying instead on expensive and time-consuming manual tuning procedures. In this work, we propose a technique for the automated estimation of outlet boundary conditions based on optimal control. The values of resistive boundary conditions are set as control variables and optimized to match available patient-specific data. Experimental results on four aortic arches demonstrate that the proposed framework can assimilate 4D-Flow MRI data more accurately than two other common techniques based on Murray's law and Ohm's law.

中文翻译：

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从心血管模拟的体内数据确定阻力型边界条件的最佳控制方法

选择合适的边界条件是心血管系统计算流体动力学 (CFD) 模拟的基本步骤。事实上，边界条件会极大地影响计算得出的压力和流速，从而影响临床意义的血流动力学指标，例如壁剪应力 (WSS)。设计用于选择边界条件的自动化程序对于实现可重复的模拟至关重要。然而，最常见的技术不会自动吸收特定于患者的数据，而是依赖于昂贵且耗时的手动调整程序。在这项工作中，我们提出了一种基于最优控制自动估计出口边界条件的技术。电阻边界条件的值被设置为控制变量并优化以匹配可用的患者特定数据。四个主动脉弓的实验结果表明，所提出的框架可以比基于默里定律和欧姆定律的其他两种常见技术更准确地吸收 4D-Flow MRI 数据。