  Image reconstruction from undersampled k-space data has been playing an
important role for fast MRI. Recently, deep learning has demonstrated
tremendous success in various fields and also shown potential to significantly
speed up MR reconstruction with reduced measurements. This article gives an
overview of deep learning-based image reconstruction methods for MRI. Three
types of deep learning-based approaches are reviewed, the data-driven,
model-driven and integrated approaches. The main structure of each network in
three approaches is explained and the analysis of common parts of reviewed
networks and differences in-between are highlighted. Based on the review, a
number of signal processing issues are discussed for maximizing the potential
of deep reconstruction for fast MRI. the discussion may facilitate further
development of "optimal" network and performance analysis from a theoretical
point of view.
