  Magnetic Resonance Imaging (MRI) is a vital component of medical imaging.
When compared to other image modalities, it has advantages such as the absence
of radiation, superior soft tissue contrast, and complementary multiple
sequence information. However, one drawback of MRI is its comparatively slow
scanning and reconstruction compared to other image modalities, limiting its
usage in some clinical applications when imaging time is critical. Traditional
compressive sensing based MRI (CS-MRI) reconstruction can speed up MRI
acquisition, but suffers from a long iterative process and noise-induced
artefacts. Recently, Deep Neural Networks (DNNs) have been used in sparse MRI
reconstruction models to recreate relatively high-quality images from heavily
undersampled k-space data, allowing for much faster MRI scanning. However,
there are still some hurdles to tackle. For example, directly training DNNs
based on L1/L2 distance to the target fully sampled images could result in
blurry reconstruction because L1/L2 loss can only enforce overall image or
patch similarity and does not take into account local information such as
anatomical sharpness. It is also hard to preserve fine image details while
maintaining a natural appearance. More recently, Generative Adversarial
Networks (GAN) based methods are proposed to solve fast MRI with enhanced image
perceptual quality. The encoder obtains a latent space for the undersampling
image, and the image is reconstructed by the decoder using the GAN loss. In
this chapter, we review the GAN powered fast MRI methods with a comparative
study on various anatomical datasets to demonstrate the generalisability and
robustness of this kind of fast MRI while providing future perspectives.
