Super-resolution reconstruction of MR image with self-attention based generate adversarial network algorithm

Super-resolution (SR) MRI images can provide fine-grained anatomical information, however it takes a long time to acquire data. In order to accelerate the acquisition of MR images while maintaining high-quality images, extensive research has been performed on image reconstruction through the deep learning method. In this study, a reconstruction framework by using self-attention based super-resolution generative adversarial networks (SA-SR-GAN) is proposed to generate super resolution MR image from low resolution MR image. Moreover, the self-attention mechanism is integrated into super-resolution generative adversarial networks (SR-GAN) framework, which is used to calculate the weight parameters of the input features. At the same time, spectral normalization is added to make the discriminator network training process more stable. The network was trained with 40 3D images (each 3D image contains 256 slices) and tested with 10 images. The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed SA-SR-GAN method are higher than the state-of-the-art reconstruction methods.