A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

中文翻译：

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计算血流动力学模型参数顺序估计的灵活框架

构建三维患者特定血液动力学模型的主要挑战是模型参数的校准，以匹配在临床中获得的关于流量、压力、壁运动等的患者数据。当前的工作流程是手动且耗时的。这项工作为心血管血流模型参数估计提供了一个灵活的计算框架，该框架依赖于以下基本贡献。(i) 用于壁材料和简单集总参数网络 (LPN) 边界条件模型参数的数据同化的降阶无迹卡尔曼滤波器 (ROUKF) 模型。(ii) 用于更复杂 LPN 的约束最小二乘增强 (ROUKF-CLS)。(iii) “网表”实现，支持在这种复杂的 LPN 中轻松过滤参数。ROUKF 算法使用来自健康志愿者的非侵入性患者特定的解剖结构、流量和压力数据进行演示。ROUKF-CLS 算法使用冠状动脉 LPN 上的合成数据进行演示。本文中描述的方法已作为 CRIMSON 血流动力学软件包的一部分实施。