Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations.
Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations.
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DOI：
10.3389/fcvm.2021.742110
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发表时间：
2021
影响因子：
3.6
通讯作者：
Obrist D
中科院分区：
文献类型：
--
作者：
De Marinis D;Obrist D
我们提出了一种数据同化方法，可用于提高空间和时间分辨率的体素为基础的数据，因为它可以从生物医学成像模式。通过将观测数据与计算流体动力学求解器相结合，可用于改善大血管中湍流血流的评估。该方法是基于一个随机Enhancement卡尔曼滤波器（SEnKF）的方法和面向脉动和湍流配置。我们用一个平均值及其协方差来描述观测到的流场。这些流场与从流场的直接数值模拟获得的预测相结合。该方法对规范的脉动和湍流进行了验证。最后，将其应用于临床相关配置，即主动脉体模中生物瓣膜下游的流动。它演示了如何从实验观测得到的4D流场可以增强的数据同化算法。结果表明，所提出的方法是有前途的，在未来使用的体内数据从4D流动磁共振成像（4D Flow MRI）。4D Flow MRI返回受工具空间和时间分辨率限制的空间和时间平均流场。这些平均流场和相关的不确定性可能被用作所提出的方法的背景下的观测数据。
We propose a data assimilation methodology that can be used to enhance the spatial and temporal resolution of voxel-based data as it may be obtained from biomedical imaging modalities. It can be used to improve the assessment of turbulent blood flow in large vessels by combining observed data with a computational fluid dynamics solver. The methodology is based on a Stochastic Ensemble Kalman Filter (SEnKF) approach and geared toward pulsatile and turbulent flow configurations. We describe the observed flow fields by a mean value and its covariance. These flow fields are combined with forecasts obtained from a direct numerical simulation of the flow field. The method is validated against canonical pulsatile and turbulent flows. Finally, it is applied to a clinically relevant configuration, namely the flow downstream of a bioprosthetic valve in an aorta phantom. It is demonstrated how the 4D flow field obtained from experimental observations can be enhanced by the data assimilation algorithm. Results show that the presented method is promising for future use with in vivo data from 4D Flow Magnetic Resonance Imaging (4D Flow MRI). 4D Flow MRI returns spatially and temporally averaged flow fields that are limited by the spatial and the temporal resolution of the tool. These averaged flow fields and the associated uncertainty might be used as observation data in the context of the proposed methodology.
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影响因子：
1.9
作者：
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