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Abstract #3537

USR-Net: A Simple Unsupervised Single-Image Super-Resolution Method for Late Gadolinium Enhancement CMR

Jin Zhu1, Guang Yang2,3, Tom Wong2,3, Raad Mohiaddin2,3, David Firmin2,3, Jennifer Keegan2,3, and Pietro Lio1
1Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom, 2Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, 3National Heart and Lung Institute, Imperial College London, London, United Kingdom

Three-dimensional late gadolinium enhanced (LGE) CMR plays an important role in scar tissue detection in patients with atrial fibrillation. Although high spatial resolution and contiguous coverage lead to a better visualization of the thin-walled left atrium and scar tissues, markedly prolonged scanning time is required for spatial resolution improvement. In this paper, we propose a convolutional neural network based unsupervised super-resolution method, namely USR-Net, to increase the apparent spatial resolution of 3D LGE data without increasing the scanning time. Our USR-Net can achieve robust and comparable performance with state-of-the-art supervised methods which require a large amount of additional training images.

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