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Spieker, V. ; Eichhorn, H. ; Hammernik, K. ; Rueckert, D.* ; Preibisch, C.* ; Karampinos, D.C.* ; Schnabel, J.A.

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review.

IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2023.3323215 (2023)
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Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.
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Publication type Article: Journal article
Document type Review
Keywords Deep Learning ; Deep Learning ; Image Reconstruction ; Loss Measurement ; Magnetic Resonance Imaging ; Motion Artefacts ; Motion Compensation ; Motion Compensation ; Motion Correction ; Motion Detection ; Motion Simulation ; Mri ; Training
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Journal IEEE Transactions on Medical Imaging
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place New York, NY [u.a.]
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1109/TMI.2023.3323215
Scopus ID 85174856232
PubMed ID 37831582
Erfassungsdatum 2023-12-12
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