Abstract #2028

A Single-Image Super-Resolution Method for Late Gadolinium Enhancement CMR

Jin Zhu¹, Guang Yang^2,3, Tom Wong^2,3, Raad Mohiaddin ^2,3, David Firmin^2,3, Jennifer Keegan ^2,3, and Pietro Lio¹

¹Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom, ²National Heart and Lung Institute, Imperial College London, London, United Kingdom, ³Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom

3D late gadolinium enhanced (LGE) CMR is a useful imaging modality for detecting scar tissue in patients with atrial fibrillation. In order to visualize the thin-walled left atrium and scar tissue, high spatial resolution and contiguous coverage are required. However, increased spatial resolution requires markedly prolonged scanning time. In this paper, we propose a ROI focused single-image super-resolution (SISR) method based on the generative adversarial networks architecture to increase the apparent spatial resolution of 3D LGE data without increasing scan time. The proposed SISR method can boost the spatial resolution of the LGE CMR images while maintaining the perceptual quality.

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