IRUS Total

FA-GAN: fused attentive generative adversarial networks for MRI image super-resolution

File Description SizeFormat 
1-s2.0-S089561112100118X-main.pdfPublished version6.07 MBAdobe PDFView/Open
Title: FA-GAN: fused attentive generative adversarial networks for MRI image super-resolution
Authors: Jiang, M
Zhi, M
Wei, L
Yang, X
Zhang, J
Li, Y
Wang, P
Huang, J
Yang, G
Item Type: Journal Article
Abstract: High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super- resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images. In the framework of the FA-GAN, the local fusion feature block, consisting of different three-pass networks by using different convolution kernels, is proposed to extract image features at different scales. And the global feature fusion module, including the channel attention module, the self-attention module, and the fusion operation,is designed to enhance the important features of the MR image. Moreover, the spectral normalization process is introduced to make the discriminator network stable. 40 sets of 3D magnetic resonance images (each set of images contains 256 slices) are used to train the network, and 10 sets of images are used to test the proposed method. The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed FA-GAN method are higher than the state-of-the-art reconstruction methods.
Issue Date: Sep-2021
Date of Acceptance: 6-Aug-2021
URI: http://hdl.handle.net/10044/1/90992
DOI: 10.1016/j.compmedimag.2021.101969
ISSN: 0895-6111
Publisher: Elsevier
Start Page: 1
End Page: 11
Journal / Book Title: Computerized Medical Imaging and Graphics
Volume: 92
Copyright Statement: © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Sponsor/Funder: British Heart Foundation
European Research Council Horizon 2020
Commission of the European Communities
Innovative Medicines Initiative
Medical Research Council (MRC)
Funder's Grant Number: PG/16/78/32402
H2020-SC1-FA-DTS-2019-1 952172
Keywords: Attention
Generative adversarial networks
Nuclear Medicine & Medical Imaging
0903 Biomedical Engineering
1103 Clinical Sciences
Publication Status: Published
Online Publication Date: 2021-08-10
Appears in Collections:National Heart and Lung Institute
Faculty of Medicine

This item is licensed under a Creative Commons License Creative Commons