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Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantlyaccelerates MR data acquisition at a sampling rate much lower than the Nyquistcriterion. A major challenge for CS-MRI lies in solving the severely ill-posedinverse problem to reconstruct aliasing-free MR images from the sparse k-spacedata. Conventional methods typically optimize an energy function, producingreconstruction of high quality, but their iterative numerical solversunavoidably bring extremely slow processing. Recent data-driven techniques areable to provide fast restoration by either learning direct prediction to finalreconstruction or plugging learned modules into the energy optimizer.Nevertheless, these data-driven predictors cannot guarantee the reconstructionfollowing constraints underlying the regularizers of conventional methods sothat the reliability of their reconstruction results are questionable. In thispaper, we propose a converged deep framework assembling principled modules forCS-MRI that fuses learning strategy with the iterative solver of a conventionalreconstruction energy. This framework embeds an optimal condition checkingmechanism, fostering \emph{efficient} and \emph{reliable} reconstruction. Wealso apply the framework to two practical tasks, \emph{i.e.}, parallel imagingand reconstruction with Rician noise. Extensive experiments on both benchmarkand manufacturer-testing images demonstrate that the proposed method reliablyconverges to the optimal solution more efficiently and accurately than thestate-of-the-art in various scenarios.

Converged Deep Framework Assembling Principled Modules for CS-MRI