  Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management
and monitoring of many diseases. However, it is an inherently slow imaging
technique. Over the last 20 years, parallel imaging, temporal encoding and
compressed sensing have enabled substantial speed-ups in the acquisition of MRI
data, by accurately recovering missing lines of k-space data. However, clinical
uptake of vastly accelerated acquisitions has been limited, in particular in
compressed sensing, due to the time-consuming nature of the reconstructions and
unnatural looking images. Following the success of machine learning in a wide
range of imaging tasks, there has been a recent explosion in the use of machine
learning in the field of MRI image reconstruction. A wide range of approaches
have been proposed, which can be applied in k-space and/or image-space.
Promising results have been demonstrated from a range of methods, enabling
natural looking images and rapid computation. In this review article we
summarize the current machine learning approaches used in MRI reconstruction,
discuss their drawbacks, clinical applications, and current trends.
