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Magnetic resonance imaging is a powerful imaging modality that can provideversatile information but it has a bottleneck problem "slow imaging speed".Reducing the scanned measurements can accelerate MR imaging with the aid ofpowerful reconstruction methods, which have evolved from linear analytic modelsto nonlinear iterative ones. The emerging trend in this area is replacinghuman-defined signal models with that learned from data. Specifically, from2016, deep learning has been incorporated into the fast MR imaging task, whichdraws valuable prior knowledge from big datasets to facilitate accurate MRimage reconstruction from limited measurements. This survey aims to review deeplearning based MR image reconstruction works from 2016- June 2020 and willdiscuss merits, limitations and challenges associated with such methods. Lastbut not least, this paper will provide a starting point for researchersinterested in contributing to this field by pointing out good tutorialresources, state-of-the-art open-source codes and meaningful data sources.

Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data