  Compressed sensing (CS) has been introduced to accelerate data acquisition in
MR Imaging. However, CS-MRI methods suffer from detail loss with large
acceleration and complicated parameter selection. To address the limitations of
existing CS-MRI methods, a model-driven MR reconstruction is proposed that
trains a deep network, named CP-net, which is derived from the Chambolle-Pock
algorithm to reconstruct the in vivo MR images of human brains from highly
undersampled complex k-space data acquired on different types of MR scanners.
The proposed deep network can learn the proximal operator and parameters among
the Chambolle-Pock algorithm. All of the experiments show that the proposed
CP-net achieves more accurate MR reconstruction results, outperforming
state-of-the-art methods across various quantitative metrics.
