We introduce an effective strategy to maximize the potential of deep learning and model-based reconstruction based on the network of ISTA-net, which is the unrolled version of iterative shrinkage-thresholding algorithm for compressed sensing reconstruction. By relaxing the constraints in the reconstruction model and the algorithm, the reconstruction quality is expected to be better. The prior of the to-be-reconstructed image is obtained by the trained networks and the data consistency is also maintained through updating in k-space for the reconstruction. Brain data shows the effectiveness of the proposed strategy.
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