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Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review.
IEEE Transactions on Medical Imaging
|October 13, 2023
Summary
Deep learning methods offer solutions for motion correction in magnetic resonance imaging (MRI). This review categorizes these techniques, highlighting challenges and future directions for improved motion artifact reduction in MRI.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Biomedical Engineering
Background:
- Motion is a significant challenge in magnetic resonance imaging (MRI), causing complex artifacts.
- Artifacts arise from object motion in frequency space during MR signal acquisition.
- Existing deep learning approaches for MRI motion correction vary widely.
Conclusions:
- Further interaction between research fields can enhance MRI motion correction.
- Standardized evaluation and diverse datasets are needed for robust deep learning models.
- Future work should focus on generalizable and adaptable deep learning solutions for MRI motion artifacts.
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