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10 September 2019 Super-resolution MRI and CT through GAN-CIRCLE
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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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qing Lyu, Chenyu You, Hongming Shan, Yi Zhang, and Ge Wang "Super-resolution MRI and CT through GAN-CIRCLE", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111130X (10 September 2019);

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