Stop moving: MR motion correction as an opportunity for artificial intelligence

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion cor...

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Bibliographic Details
Published in:Magma (New York, N.Y.) Vol. 37; no. 3; pp. 397 - 409
Main Authors: Zhou, Zijian, Hu, Peng, Qi, Haikun
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.07.2024
Subjects:
Algorithms
Artificial Intelligence
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Brain - diagnostic imaging
Computer Appl. in Life Sciences
Deep Learning
Health Informatics
Humans
Image Processing, Computer-Assisted - methods
Imaging
Magnetic Resonance Imaging - methods
Medicine
Medicine & Public Health
Motion
Movement
Neural Networks, Computer
Radiology
Review
Solid State Physics
Motion correction
Motion estimation
MRI reconstruction
Artificial intelligence
ISSN:1352-8661, 1352-8661
Online Access:Get full text
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Summary:Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.
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ISSN:1352-8661
1352-8661
DOI:10.1007/s10334-023-01144-5