Deep learning has shown great success in MR image segmentation, enhancement and reconstruction. However, most methods, if not all, rely on a pair of the input image and the ground-truth image to train the network for a given task. In practice, it is often hard to get the corresponding ground-truth MR images due to limitations in data acquisition. In this study, we aim to use the convolutional neural network (CNN) structure itself as a constraint without using ground-truth images in an optimization task and to evaluate its performance in MR image denoising and super-resolution applications.
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