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Magnetic Resonance Imaging (MRI) is a vital component of medical imaging.When compared to other image modalities, it has advantages such as the absenceof radiation, superior soft tissue contrast, and complementary multiplesequence information. However, one drawback of MRI is its comparatively slowscanning and reconstruction compared to other image modalities, limiting itsusage in some clinical applications when imaging time is critical. Traditionalcompressive sensing based MRI (CS-MRI) reconstruction can speed up MRIacquisition, but suffers from a long iterative process and noise-inducedartefacts. Recently, Deep Neural Networks (DNNs) have been used in sparse MRIreconstruction models to recreate relatively high-quality images from heavilyundersampled k-space data, allowing for much faster MRI scanning. However,there are still some hurdles to tackle. For example, directly training DNNsbased on L1/L2 distance to the target fully sampled images could result inblurry reconstruction because L1/L2 loss can only enforce overall image orpatch similarity and does not take into account local information such asanatomical sharpness. It is also hard to preserve fine image details whilemaintaining a natural appearance. More recently, Generative AdversarialNetworks (GAN) based methods are proposed to solve fast MRI with enhanced imageperceptual quality. The encoder obtains a latent space for the undersamplingimage, and the image is reconstructed by the decoder using the GAN loss. Inthis chapter, we review the GAN powered fast MRI methods with a comparativestudy on various anatomical datasets to demonstrate the generalisability androbustness of this kind of fast MRI while providing future perspectives.

Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives