Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images

TL;DR

This paper introduces a novel deep learning model combining Transformer and GAN techniques for super-resolution reconstruction of medical images, significantly improving texture detail and image quality in MRI scans.

Contribution

The paper proposes a new T-GAN model that integrates Transformer with GAN for enhanced medical image super-resolution, focusing on texture detail and global image matching.

Findings

T-GAN outperforms existing methods in PSNR and SSIM metrics.

The model effectively recovers detailed textures in MRI images.

It demonstrates superior performance on knee and belly MRI datasets.

Abstract

Because of the necessity to obtain high-quality images with minimal radiation doses, such as in low-field magnetic resonance imaging, super-resolution reconstruction in medical imaging has become more popular (MRI). However, due to the complexity and high aesthetic requirements of medical imaging, image super-resolution reconstruction remains a difficult challenge. In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like PSNR and SSIM, our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.