Recent improvements in magnetic resonance image (MRI) reconstruction from partial data have been reportedusing spatial context modelling with Markov random field (MRF) priors. However, these algorithms have beendeveloped only for magnitude images from single-coil measurements. In practice, most of the MRI images todayare acquired using multi-coil data. In this paper, we extend our recent approach for MRI reconstruction withMRF priors to deal with multi-coil data i.e., to be applicable in parallel MRI (pMRI) settings. Instead ofreconstructing images from different coils independently and subsequently combining them into the final image,we recover MRI image by processing jointly the undersampled measurements from all coils together with theirestimated sensitivity maps. The proposed method incorporates a Bayesian formulation of the spatial contextinto the reconstruction problem. To solve the resulting problem, we derive an efficient algorithm based onthe alternating direction method of multipliers (ADMM). Experimental results demonstrate the effectiveness ofthe proposed approach in comparison to some well-adopted methods for accelerated pMRI reconstruction fromundersampled data.
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