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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)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter 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
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
DOI 10.1109/TMI.2023.3323215
Scopus ID 85174856232
PubMed ID 37831582
Erfassungsdatum 2023-12-12
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