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

Chen Y, Schonlieb CB, Lio P, Leiner T, Dragotti PL, Wang G, Rueckert D, Firmin D, Yang G (2022)

Publication Type: Journal article, Review article

Publication year: 2022

Journal

Proceedings of the IEEE Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 110

Pages Range: 224-245

Journal Issue: 2

DOI: 10.1109/JPROC.2022.3141367

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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine

United Kingdom (GB) Rensselaer Polytechnic Institute (RPI)

United States (USA) (US) University of Cambridge

United Kingdom (GB) Universiteit Utrecht (UU) / Utrecht University

Netherlands (NL)

How to cite

APA:

Chen, Y., Schonlieb, C.-B., Lio, P., Leiner, T., Dragotti, P.L., Wang, G.,... Yang, G. (2022). AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis. Proceedings of the IEEE, 110(2), 224-245. https://doi.org/10.1109/JPROC.2022.3141367

MLA:

Chen, Yutong, et al. "AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis." Proceedings of the IEEE 110.2 (2022): 224-245.

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