  Compressed sensing (CS) has been playing a key role in accelerating the
magnetic resonance imaging (MRI) acquisition process. With the resurgence of
artificial intelligence, deep neural networks and CS algorithms are being
integrated to redefine the state of the art of fast MRI. The past several years
have witnessed substantial growth in the complexity, diversity, and performance
of deep learning-based CS techniques that are dedicated to fast MRI. In this
meta-analysis, we systematically review the deep learning-based CS techniques
for fast MRI, describe key model designs, highlight breakthroughs, and discuss
promising directions. We have also introduced a comprehensive analysis
framework and a classification system to assess the pivotal role of deep
learning in CS-based acceleration for MRI.
