Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach
Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach
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DOI：
10.1016/j.compfluid.2019.104391
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发表时间：
2020-01
影响因子：
2.8
通讯作者：
Fabian Klemens;S. Schuhmann;Roland Balbierer;G. Guthausen;H. Nirschl;G. Thäter;M. Krause
中科院分区：
文献类型：
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作者：
Fabian Klemens;S. Schuhmann;Roland Balbierer;G. Guthausen;H. Nirschl;G. Thäter;M. Krause
在以前的工作中，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。使用基于格子玻尔兹曼的拓扑优化方法，该方法可以被描述为用于流动MRI测量的Navier-Stokes滤波器。本文的主要目的是分析和量化CFD-MRI的能力，以减少统计测量噪声。为此，分析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，并且可以执行彻底的分析。结果表明，与原始数据非常一致，即使在输入数据中有很高的统计噪声和有限的信息。
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.