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Titel:

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

Dokumenttyp:
Journal Article
Autor(en):
Spieker, Veronika; Eichhorn, Hannah; Hammernik, Kerstin; Rueckert, Daniel; Preibisch, Christine; Karampinos, Dimitrios C; Schnabel, Julia A
Abstract:
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. deformab...     »
Zeitschriftentitel:
IEEE Trans Med Imaging
Jahr:
2024
Band / Volume:
43
Heft / Issue:
2
Seitenangaben Beitrag:
846-859
Volltext / DOI:
doi:10.1109/TMI.2023.3323215
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/37831582
Print-ISSN:
0278-0062
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Institut für KI und Informatik in der Medizin (Prof. Rückert)
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