Noise Modelling in Parallel Magnetic Resonance Imaging: A Variational Approach

摘要

We proposed a new variational model for parallel Magnetic Resonance Imaging (MRI) processing including denoising, deblurring and super-resolution. In the context of Maximum A Posteriori (MAP) estimation it takes into account the non-central χ (nc-χ) distribution of the noise in parallel magnitude magnetic resonance (MR) images. This leads to the resolution of an energy minimization problem. In this Bayesian modelling framework the Total Generalized Variation (TGV) is proposed as the regularization term. A primal-dual algorithm is then implemented to solve numerically the presented model. The effectiveness of our approach is shown through a successful comparison of its performance to previous TGV methods for MRI denoising based on Gaussian noise.