Abstract In a previous work, the feasibility of coupling magnetic resonance imaging (MRI) measurements and computational fluid dynamics (CFD) was presented, called CFD-MRI. Using a lattice Boltzmann based topology optimisation approach, the method can be described as a Navier–Stokes filter for flow MRI measurements. The main objective of this article is the analysis and quantification of CFD-MRI for its ability to reduce statistical measurement noise. For this, MRI data was analysed and used as basis for synthetic data, where noise was added to a simulation result. Thus, the noise-free data is known and a thorough analysis can be performed. The results show a very high agreement with the original data, even with high statistical noise in the input data and limited information available.

中文翻译：

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使用基于格子 Boltzmann 的拓扑优化方法降低流量 MRI 测量的噪声

摘要 在之前的工作中，提出了耦合磁共振成像 (MRI) 测量和计算流体动力学 (CFD) 的可行性，称为 CFD-MRI。使用基于格子 Boltzmann 的拓扑优化方法，该方法可以描述为用于流 MRI 测量的 Navier-Stokes 滤波器。本文的主要目的是分析和量化 CFD-MRI 降低统计测量噪声的能力。为此，分析了 MRI 数据并将其用作合成数据的基础，其中将噪声添加到模拟结果中。因此，无噪声数据是已知的，并且可以进行彻底的分析。结果显示与原始数据的一致性非常高，即使输入数据中的统计噪声很高且可用信息有限。