AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis | IEEE Journals & Magazine | IEEE Xplore

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis


Abstract:

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligen...Show More

Abstract:

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.
Published in: Proceedings of the IEEE ( Volume: 110, Issue: 2, February 2022)
Page(s): 224 - 245
Date of Publication: 03 February 2022

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