  Magnetic resonance imaging (MRI) is a widely used non-radiative and
non-invasive method for clinical interrogation of organ structures and
metabolism, with an inherently long scanning time. Methods by k-space
undersampling and deep learning based reconstruction have been popularised to
accelerate the scanning process. This work focuses on investigating how
powerful transformers are for fast MRI by exploiting and comparing different
novel network architectures. In particular, a generative adversarial network
(GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI
reconstruction. To further preserve the edge and texture information, edge
enhanced GAN based Swin transformer (EES-GAN) and texture enhanced GAN based
Swin transformer (TES-GAN) were also developed, where a dual-discriminator GAN
structure was applied. We compared our proposed GAN based transformers,
standalone Swin transformer and other convolutional neural networks based GAN
model in terms of the evaluation metrics PSNR, SSIM and FID. We showed that
transformers work well for the MRI reconstruction from different undersampling
conditions. The utilisation of GAN's adversarial structure improves the quality
of images reconstructed when undersampled for 30% or higher. The code is
publicly available at https://github.com/ayanglab/SwinGANMR.
