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使用离体 MRI 速度数据进行 CFD 验证 - 数据匹配和 CFD 误差量化方法。

CFD validation using in-vitro MRI velocity data - Methods for data matching and CFD error quantification.

机构信息

Institute of Fluid Mechanics, University of Rostock, Justus-von-Liebig-Weg 2, 18059, Rostock, Germany.

Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, 18057, Rostock, Germany.

出版信息

Comput Biol Med. 2021 Apr;131:104230. doi: 10.1016/j.compbiomed.2021.104230. Epub 2021 Jan 20.

DOI:10.1016/j.compbiomed.2021.104230
PMID:33545507
Abstract

Predicting blood flow velocities in patient-specific geometries with Computational Fluid Dynamics (CFD) can provide additional data for diagnosis and treatment planning but the solution can be inaccurate. Therefore, it is crucial to understand the simulation errors and calibrate the numerical model. In-vitro velocity-encoded MRI is a versatile tool to validate CFD. The comparison between CFD and in-vitro MRI velocity data, and the analysis of the simulation error are the objectives of this study. A three-step routine is presented to validate medical CFD data. First, a properly scaled model of the patient-specific geometry is fabricated to achieve high relative resolution in the MRI experiment. Second, the measured flow geometry is matched with the CFD data using one of two algorithms, Coherent Point Drift and Iterative Closest Point. The aligned data sets are then interpolated onto a common grid to enable a point-to-point comparison. Third, the global and local deviations between CFD and MRI velocity data are calculated using different algorithms to reliably estimate the simulation error. The routine is successfully tested with a patient-specific model of a cerebral aneurysm. In conclusion, the methods presented here provide a framework for CFD validation using in-vitro MRI velocity data.

摘要

使用计算流体动力学 (CFD) 预测特定于患者的几何形状中的血流速度可以提供额外的数据用于诊断和治疗计划,但解决方案可能不准确。因此,了解模拟误差并校准数值模型至关重要。体外速度编码 MRI 是验证 CFD 的多功能工具。本研究的目的是比较 CFD 和体外 MRI 速度数据,并分析模拟误差。提出了一个三步程序来验证医学 CFD 数据。首先,制造适当缩放的患者特定几何形状模型,以在 MRI 实验中实现高相对分辨率。其次,使用两个算法之一,相干点漂移和迭代最近点,将测量的流形与 CFD 数据匹配。然后将对齐的数据集插值到公共网格上,以实现点对点比较。第三,使用不同的算法计算 CFD 和 MRI 速度数据之间的全局和局部偏差,以可靠地估计模拟误差。该程序已成功用于脑动脉瘤的患者特定模型进行测试。总之,这里提出的方法为使用体外 MRI 速度数据验证 CFD 提供了一个框架。

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