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

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 yea...

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Bibliographic Details
Published in:Proceedings of the IEEE 2022-02, Vol.110 (2), p.224-245
Main Authors: Chen, Yutong, Schonlieb, Carola-Bibiane, Lio, Pietro, Leiner, Tim, Dragotti, Pier Luigi, Wang, Ge, Rueckert, Daniel, Firmin, David, Yang, Guang
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Complexity theory
Compressed sensing
Compressed sensing (CS)
Deep learning
Machine learning
Magnetic resonance imaging
magnetic resonance imaging (MRI)
Meta-analysis
neural network
Neural networks
Systematics
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Summary: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.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2022.3141367