  Magnetic resonance imaging is a powerful imaging modality that can provide
versatile information but it has a bottleneck problem "slow imaging speed".
Reducing the scanned measurements can accelerate MR imaging with the aid of
powerful reconstruction methods, which have evolved from linear analytic models
to nonlinear iterative ones. The emerging trend in this area is replacing
human-defined signal models with that learned from data. Specifically, from
2016, deep learning has been incorporated into the fast MR imaging task, which
draws valuable prior knowledge from big datasets to facilitate accurate MR
image reconstruction from limited measurements. This survey aims to review deep
learning based MR image reconstruction works from 2016- June 2020 and will
discuss merits, limitations and challenges associated with such methods. Last
but not least, this paper will provide a starting point for researchers
interested in contributing to this field by pointing out good tutorial
resources, state-of-the-art open-source codes and meaningful data sources.
