FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution.
FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution.
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FA-GAN：用于 MRI 图像超分辨率的融合注意力生成对抗网络
DOI：
10.1016/j.compmedimag.2021.101969
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发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Yang G
中科院分区：
文献类型：
--
作者：
Jiang M;Zhi M;Wei L;Yang X;Zhang J;Li Y;Wang P;Huang J;Yang G
提出了一种融合注意生成对抗网络的MR图像超分辨率框架。信道注意力和自注意力的组合被用来计算输入特征的权重参数。引入谱归一化处理，使神经网络更加稳定。所提出的FA-GAN方法是上级的国家的最先进的重建方法。高分辨率磁共振图像可以提供细粒度的解剖信息，但获取这样的数据需要很长的扫描时间。本文提出了一种融合注意生成对抗网络（FA-GAN）框架，用于从低分辨率磁共振图像生成超分辨率MR图像，该框架可以有效地减少扫描时间，但需要高分辨率MR图像。在FA-GAN框架下，提出了由不同卷积核的三遍网络组成的局部融合特征块，用于提取不同尺度下的图像特征。设计了全局特征融合模块，包括通道关注模块、自关注模块和融合操作，用于增强MR图像的重要特征。此外，频谱归一化过程中引入，使网络的稳定。使用40组3D磁共振图像（每组图像包含256个切片）来训练网络，并使用10组图像来测试所提出的方法。实验结果表明，该方法生成的超分辨磁共振图像的PSNR和SSIM值均高于现有的重建方法。
A fused attentive generative adversarial networks framework is proposed for MR image super-resolution. A combination of channel attention and self-attention is used to calculate the weight parameters of the input features. Spectral normalization process is introduced to make the discriminator network stabler. The proposed FA-GAN method is superior to the state-of-the-art reconstruction methods. 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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作者：
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DOI：
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DOI：
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发表时间：
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期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
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作者：
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通讯作者：
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