Imperial College London
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ProfessorDavidFirmin

Faculty of Medicine, National Heart & Lung Institute

Emeritus Professor of Biomedical Imaging
 
 
 
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Contact

 

+44 (0)20 7351 8801d.firmin

 
 
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Location

 

Cardiovascular MR UnitRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chen:2022:10.1109/JPROC.2022.3141367,
author = {Chen, Y and Schönlieb, C-B and Liò, P and Leiner, T and Dragotti, PL and Wang, G and Rueckert, D and Firmin, D and Yang, G},
doi = {10.1109/JPROC.2022.3141367},
journal = {Proceedings of the Institute of Electrical and Electronics Engineers (IEEE)},
pages = {224--245},
title = {AI-based reconstruction for fast MRI – a systematic review and meta-analysis},
url = {http://dx.doi.org/10.1109/JPROC.2022.3141367},
volume = {110},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 fastMRI. 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 accelerationfor MRI.
AU  - Chen,Y
AU  - Schönlieb,C-B
AU  - Liò,P
AU  - Leiner,T
AU  - Dragotti,PL
AU  - Wang,G
AU  - Rueckert,D
AU  - Firmin,D
AU  - Yang,G
DO  - 10.1109/JPROC.2022.3141367
EP  - 245
PY  - 2022///
SN  - 0018-9219
SP  - 224
TI  - AI-based reconstruction for fast MRI – a systematic review and meta-analysis
T2  - Proceedings of the Institute of Electrical and Electronics Engineers (IEEE)
UR  - http://dx.doi.org/10.1109/JPROC.2022.3141367
UR  - https://ieeexplore.ieee.org/document/9703109
UR  - http://hdl.handle.net/10044/1/93694
VL  - 110
ER  -
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