Deep learning has recently been applied to image reconstruction from undersampled k-space data with success. Most existing works require both undersampled data and ground truth image as the training pair. It is not practical to obtain a large number of ground truth images for training in some MR applications. Here a novel deep learning network is studied for image reconstruction using only undersampled data for training. Experiment results demonstrate the feasibility of training without the ground truth images for image reconstruction.
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