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

TL;DR

FA-GAN is a novel framework that generates high-resolution MRI images from low-resolution scans, reducing scan time while maintaining image quality through advanced feature fusion and attention mechanisms.

Contribution

The paper introduces FA-GAN, which combines local and global feature fusion with attention modules and spectral normalization for improved MRI super-resolution.

Findings

Higher PSNR and SSIM compared to state-of-the-art methods

Effective reduction of MRI scanning time

Enhanced feature extraction with fusion and attention modules

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.