Super‐MoCo‐MoDL: A combined super-resolution and motion-corrected undersampled deep-learning reconstruction framework for three-dimensional whole-heart cardiac magnetic resonance imaging
Super-MoCo-MoDL is able to reconstruct high-resolution 3D whole-heart images from low-resolution and undersampled anisotropic acquisitions.
Authors
- Andrew Phair (ORCID: https://orcid.org/0000-0003-3527-0724)
- Claudia Prieto (ORCID: https://orcid.org/0000-0003-4602-2523)
- Simon Littlewood (ORCID: https://orcid.org/0000-0001-7433-1409)
- Lina Felsner (ORCID: https://orcid.org/0000-0001-7695-2612)
- René M. Botnar (ORCID: https://orcid.org/0000-0003-2811-2509)
- Thomas J. Fletcher (ORCID: https://orcid.org/0000-0002-1633-1117)
- Anastasia Fotaki (ORCID: https://orcid.org/0000-0002-0353-5778)
- Won-Yong Kim
Institutions
- Pontificia Universidad Católica de Chile (CL)
- King's College London (GB)
- Aarhus University Hospital (DK)
- Millennium Institute on Immunology and Immunotherapy (CL)
- Technical University of Munich (DE)
Publication Details
- Journal
- Journal of Cardiovascular Magnetic Resonance
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.jocmr.2025.101990
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36