Coronary artery bypass grafts (CABG) surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease. In this work, we apply a numerical optimal flow control model to patient-specific geometries of CABG, reconstructed from clinical images of real-life surgical cases, in parameterized settings. The aim of these applications is to match known physiological data with numerical hemodynamics corresponding to different scenarios, arisen by tuning some parameters. Such applications are an initial step toward matching patient-specific physiological data in patient-specific vascular geometries as best as possible. Two critical challenges that reportedly arise in such problems are: (a) lack of robust quantification of meaningful boundary conditions required to match known data as best as possible and (b) high computational cost. In this work, we utilize unknown control variables in the optimal flow control problems to take care of the first challenge. Moreover, to address the second challenge, we propose a time-efficient and reliable computational environment for such parameterized problems by projecting them onto a low-dimensional solution manifold through proper orthogonal decomposition-Galerkin.

中文翻译：

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用于冠状动脉旁路移植术中参数优化流量控制的降阶方法，用于患者特定数据同化

冠状动脉旁路移植术 (CABG) 手术是一种侵入性手术，用于避免冠状动脉疾病中的部分或完全血流阻塞。在这项工作中，我们将数值最优流量控制模型应用于患者特定的 CABG 几何形状，在参数化设置中从真实手术病例的临床图像重建。这些应用程序的目的是将已知的生理数据与对应于不同场景的数值血流动力学相匹配，这些数据是通过调整一些参数而产生的。此类应用程序是在患者特定血管几何形状中尽可能匹配患者特定生理数据的第一步。据报道，在此类问题中出现的两个关键挑战是：(a) 对尽可能匹配已知数据所需的有意义的边界条件缺乏稳健的量化，以及 (b) 计算成本高。在这项工作中，我们利用最优流量控制问题中的未知控制变量来应对第一个挑战。此外，为了应对第二个挑战，我们通过适当的正交分解-Galerkin 将它们投影到低维解流形上，为此类参数化问题提出了一种省时且可靠的计算环境。