The advantages of Convolutional Neural Networks (CNN) for MRI acceleration have been widely reported, but one remaining problem is that the significantly complex network makes itself less explainable than conventional model-based methods. In this work, a novel deep learning assisted MRI acceleration method is introduced to address the uncertainty of CNN by integrating its output as another constraint into the framework of Compressed Sensing (CS).
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