FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution

• URL: http://arxiv.org/abs/2108.03920v1
• Date: Mon, 9 Aug 2021 10:21:39 GMT
• Title: FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution
• Authors: Mingfeng Jiang, Minghao Zhi, Liying Wei, Xiaocheng Yang, Jucheng Zhang, Yongming Li, Pin Wang, Jiahao Huang, Guang Yang
• Abstract summary: A framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super-resolution magnetic resonance image. 40 sets of 3D magnetic resonance images are used to train the network, and 10 sets of images are used to test the proposed method.
• Score: 8.778205385041549
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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