Many previous studies have shown that the fidelity of three-dimensional cardiovascular flow simulations depends strongly on inflow and outflow boundary conditions that accurately describe the characteristics of the larger vascular network. These boundary conditions are generally based on lower-dimensional models that represent the upstream or downstream flow behavior in some aggregated fashion. However, the parameters of these models are patient-specific, and no clear technique exists for determining them. In this work, an ensemble Kalman filter (EnKF) is implemented for the purpose of estimating parameters in cardiovascular models through the assimilation of specific patients’ clinical measurements. Two types of models are studied: a fully zero-dimensional model of the right heart and pulmonary circulation, and a coupled 0D–1D model of the lower leg. Model parameters are estimated using measurements from both healthy and hypertensive patients, and demonstrate that the EnKF is able to generate distinct parameter sets whose model predictions produce features unique to each measurement set. Attention is also given toward the quality of model predictions made in the absence of direct clinical counterparts, as well as techniques to improve filter robustness against shrinking ensemble covariance.

中文翻译：

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用于患者特定心血管血流建模的参数估计的集成卡尔曼滤波器方法

许多先前的研究表明，三维心血管血流模拟的保真度在很大程度上取决于准确描述较大血管网络特征的流入和流出边界条件。这些边界条件通常基于以某种聚合方式表示上游或下游流动行为的低维模型。然而，这些模型的参数是特定于患者的，并且没有明确的技术来确定它们。在这项工作中，集成卡尔曼滤波器 (EnKF) 被实施，目的是通过吸收特定患者的临床测量来估计心血管模型中的参数。研究了两种类型的模型：右心肺循环的完全零维模型，和小腿的耦合 0D-1D 模型。模型参数使用来自健康和高血压患者的测量值进行估计，并证明 EnKF 能够生成不同的参数集，其模型预测产生每个测量集独有的特征。还关注在没有直接临床对应物的情况下进行的模型预测的质量，以及提高滤波器对缩小集合协方差的鲁棒性的技术。