A network‐assisted joint image and motion estimation approach for robust <scp>3D MRI</scp> motion correction across severity levels
UNet+JE benefitted from the robustness of joint estimation and the fast image improvement provided by the neural network, enabling the method to provide high quality 3D image correction under a wide range of motion corruption within shorter runtimes.
Authors
- Brian Nghiem (ORCID: https://orcid.org/0000-0003-2463-9012)
- Sriranga Kashyap (ORCID: https://orcid.org/0000-0003-0307-9773)
- Kâmil Uludaǧ (ORCID: https://orcid.org/0000-0002-2813-5930)
- Lars Kasper (ORCID: https://orcid.org/0000-0001-7667-603X)
- Zhe Wu (ORCID: https://orcid.org/0000-0002-2079-5977)
Institutions
- Sunnybrook Health Science Centre (CA)
- University of Toronto (CA)
- Institute for Basic Science (KR)
- Ontario Brain Institute (CA)
- Sunnybrook Research Institute
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-08-29
- DOI
- https://doi.org/10.1002/mrm.70052
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00