TY - GEN
T1 - Super-resolution MRI and CT through GAN-CIRCLE
AU - Lyu, Qing
AU - You, Chenyu
AU - Shan, Hongming
AU - Zhang, Yi
AU - Wang, Ge
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Computed tomography (CT)
KW - Deep learning
KW - Magnetic resonance imaging (MRI)
KW - Super-resolution
UR - https://www.scopus.com/pages/publications/85077792634
U2 - 10.1117/12.2530592
DO - 10.1117/12.2530592
M3 - Conference contribution
AN - SCOPUS:85077792634
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Developments in X-Ray Tomography XII
A2 - Muller, Bert
A2 - Wang, Ge
PB - SPIE
T2 - 12th SPIE Conference on Developments in X-Ray Tomography 2019
Y2 - 13 August 2019 through 15 August 2019
ER -