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Title: Signal enhancement of low magnetic field magnetic resonance image using a conventional- and cyclic-generative adversarial network models with unpaired image sets
Authors: Yoo, D
Choi, YA
Rah, CJ
Lee, E
Cai, J 
Min, BJ
Kim, EH
Issue Date: May-2021
Source: Frontiers in oncology, May 2021, v. 11, 660284
Abstract: In this study, the signal enhancement ratio of low-field magnetic resonance (MR) images was investigated using a deep learning-based algorithm. Unpaired image sets (0.06 Tesla and 1.5 Tesla MR images for different patients) were used in this study following three steps workflow. In the first step, the deformable registration of a 1.5 Tesla MR image into a 0.06 Tesla MR image was performed to ensure that the shapes of the unpaired set matched. In the second step, a cyclic-generative adversarial network (GAN) was used to generate a synthetic MR image of the original 0.06 Tesla MR image based on the deformed or original 1.5 Tesla MR image. Finally, an enhanced 0.06 Tesla MR image could be generated using the conventional-GAN with the deformed or synthetic MR image. The results from the optimized flow and enhanced MR images showed significant signal enhancement of the anatomical view, especially in the nasal septum, inferior nasal choncha, nasopharyngeal fossa, and eye lens. The signal enhancement ratio, signal-to-noise ratio (SNR) and correlation factor between the original and enhanced MR images were analyzed for the evaluation of the image quality. A combined method using conventional- and cyclic-GANs is a promising approach for generating enhanced MR images from low-magnetic-field MR.
Keywords: Conventional-GAN
Enhancement of MR image
Low magnetic field
Magnetic Resonance Image (MRI)
Publisher: Frontiers Research Foundation
Journal: Frontiers in oncology 
EISSN: 2234-943X
DOI: 10.3389/fonc.2021.660284
Rights: © 2021 Yoo, Choi, Rah, Lee, Cai, Min and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) ( The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The following publication Yoo D, Choi YA, Rah CJ, Lee E, Cai J, Min BJ and Kim EH (2021) Signal Enhancement of Low Magnetic Field Magnetic Resonance Image Using a Conventional- and Cyclic-Generative Adversarial Network Models With Unpaired Image Sets. Front. Oncol. 11:660284 is available at
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