A Prior Learning Network for Joint Image and Sensitivity Estimation in Parallel MR Imaging

Parallel imaging is a fast magnetic resonance imaging technique through spatial sensitivity coding using multi-coils. To reconstruct a high quality MR image from under-sampled k-space data, we propose a novel deep network, dubbed as Blind-PMRI-Net, to simultaneously reconstruct the MR image and sensitivity maps in a blind setting for parallel imaging. The Blind-PMRI-Net is a novel deep architecture inspired by the iterative algorithm optimizing a novel energy model for joint image and sensitivity estimation based on image and sensitivity priors. The network is designed to be able to automatically learn these two priors by learning their corresponding proximal operators using convolutional neural networks. Blind-PMRI-Net naturally combines the physical constraint of parallel imaging and prior learning in a single deep architecture. Experiments on a knee MRI dataset show that our network can effectively reconstruct MR image with improved accuracy than previous methods, with fast computational speed. For example, Blind-PMRI-Net takes 0.72 s on GPU to reconstruct 15-channel sensitivity maps and a complex-valued MR image in size of \(320\times 320\).