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Super-resolution MRI and CT through GAN-CIRCLE
Author(s): Qing Lyu; Chenyu You; Hongming Shan; Yi Zhang; Ge Wang
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Paper Abstract

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are widely used for screening, diagnosis and imageguided therapeutics. Due to physical, technical and economical limitations, it is impossible for MRI and CT scanners to target ideal image resolution. Given the nominal imaging performance, how to improve image resolution has been a hot topic, and referred to as super-resolution research. As a promising method for super-resolution, over recent years deep learning has shown a great potential especially in deblurring natural images. In this paper, based on the neural network model termed as GAN-CIRCLE (Constrained by the Identical, Residual, Cycle Learning Ensemble), we adapt this neural network for achieving super-resolution for both MRI and CT. In this study, we demonstrate two-fold resolution enhancement for MRI and CT with the same network architecture.

Paper Details

Date Published: 10 September 2019
PDF: 7 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111130X (10 September 2019); doi: 10.1117/12.2530592
Show Author Affiliations
Qing Lyu, Rensselaer Polytechnic Institute (United States)
Chenyu You, Stanford Univ. (United States)
Hongming Shan, Rensselaer Polytechnic Institute (United States)
Yi Zhang, Sichuan Univ. (China)
Ge Wang, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)

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