AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
This is an incremental review paper that synthesizes existing research for the medical imaging community.
This paper systematically reviews and meta-analyzes deep learning-based compressed sensing techniques for accelerating MRI acquisition, highlighting their growth in complexity, diversity, and performance while introducing a new analysis framework and classification system.
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.