Brain MRI synthesis based on dual-generator generative adversarial network
Pages 1065 - 1069
Abstract
Various contrast Magnetic Resonance Imaging (MRI) of can increase the available information in clinical diagnosis. However, due to the time limit, cost, and the patient cooperation in the scanning process, some contrast images may not be obtained in time. Moreover, some images may be damaged during the sampling process. Therefore, the synthesis model is designed to obtain the missed contrast image from the existing image, which is beneficial to improve the effect of diagnosis. Hence, we propose a new brain MRI synthesis method based on generative adversarial networks (GAN). This method leverages a coarse-to-fine dual network structure based on the generative adversarial network and uses FID to make T2 weighted images generate more reliable T1 weighted images. We conducted experiments on 2730 T1 and T2 weighted images, and used structural similarity, peak signal-to-noise ratio, and mean square error as evaluation indicators. Quantitative and qualitative experimental results show that our method is superior to the original one.
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Published In
December 2022
1396 pages
ISBN:9781450398343
DOI:10.1145/3584376
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 April 2023
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RICAI 2022
RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence
December 16 - 18, 2022
Dongguan, China
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Overall Acceptance Rate 140 of 294 submissions, 48%
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