We propose a novel deep residual learning approach to reconstruct MR images from radial k-space data. We apply a transfer learning scheme that first pre-trains the network using large X-ray CT data set, and then performs a network fine-tuning using only a few MR data set. The proposed network clearly removes the streaking artifact better than other existing compressed sensing algorithm. Moreover, the computational speed is extremely faster than that of compressed sensing MRI.
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