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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