Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

中文翻译：

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SARA-GAN：用于快速压缩传感 MRI 重建的基于自我注意和相对平均鉴别器的生成对抗网络

对欠采样磁共振图像 (MRI) 重建的研究可以提高 MRI 成像的速度并减少患者的痛苦。在本文中，提出了一种基于具有自注意力机制和相对平均鉴别器的生成对抗网络（SARA-GAN）的欠采样MRI重建方法。在我们的 SARA-GAN 中，应用相对平均判别器理论来充分利用先验知识，其中判别器的输入数据一半为真，一半为假。同时在生成器的高层加入了self-attention机制来构建图像的长程依赖，可以克服卷积核大小受限的问题。此外，采用频谱归一化来稳定训练过程。