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Abstract #0472

Deep learning without ground truth: an unsupervised method for MR image denoising and super-resolution

Xue Feng1 and Craig H. Meyer1

1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States

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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