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

Teaser

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.

Publication
IEEE Transactions on Medical Imaging ( Early Access )
Veronika Spieker
Veronika Spieker
PhD Student

Veronika Spieker’s interests include AI-based methods for MR reconstrution and motion correction.

Hannah Eichhorn
Hannah Eichhorn
PhD student

Hannah Eichhorn’s research focuses on deep learning-based reconstruction of multi-parametric brain MRI.

Julia A. Schnabel
Julia A. Schnabel
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.