Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach

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. •Coupling of computational fluid dynamics and magnetic resonance images using topology optimisation.•Analysis of noise in MRI data to create synthetic data.•Quantitative analysis of noise reduction capabilities.•Application to real data.