  Deep learning (DL) has emerged as a leading approach in accelerating MR
imaging. It employs deep neural networks to extract knowledge from available
datasets and then applies the trained networks to reconstruct accurate images
from limited measurements. Unlike natural image restoration problems, MR
imaging involves physics-based imaging processes, unique data properties, and
diverse imaging tasks. This domain knowledge needs to be integrated with
data-driven approaches. Our review will introduce the significant challenges
faced by such knowledge-driven DL approaches in the context of fast MR imaging
along with several notable solutions, which include learning neural networks
and addressing different imaging application scenarios. The traits and trends
of these techniques have also been given which have shifted from supervised
learning to semi-supervised learning, and finally, to unsupervised learning
methods. In addition, MR vendors' choices of DL reconstruction have been
provided along with some discussions on open questions and future directions,
which are critical for the reliable imaging systems.
