Super-resolution Reconstruction of MRI Based on DNGAN

• Published in: Ji suan ji ke xue Vol. 49; no. 7; pp. 113 - 119
• Main Authors: Dai, Zhao-Xia, Li, Jin-Xin, Zhang, Xiang-Dong, Xu, Xu, Mei, Lin, Zhang, Liang
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.07.2022; Editorial office of Computer Science
• Subjects: Algorithms; Frequency domain analysis; Generative adversarial networks; High resolution; Image reconstruction; Interpolation; super-resolution reconstruction|generative adversarial network|magnetic resonance imaging|convolutional neural network|densenet
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210600105

The quality of MRI will affect doctor's judgment on patient's physical conditions, and the high-resolution MRI is more conducive to doctor to make an accurate diagnosis.Using computer technology to perform super-resolution reconstruction of MRI can obtain high-resolution MRI from existing low-resolution MRI.Based on the strong generation ability of the generative adversarial networks and the unsupervised learning characteristics of the generative adversarial networks, this paper studies the MRI super-resolution algorithm based on the generative adversarial networks.It designs a generative adversarial network model DNGAN that combines ResNet structure and DenseNet structure.In this network, the WGAN-GP theory is used as the adversarial loss to stabilize the training of the generative adversarial networks.In addition, the content loss function and the perceptual loss function are also used as the loss function of the network.At the same time, in order to make better use of the rich frequency domain information