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

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. 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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• Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts
• Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction
• Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI
• Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning