End-to-End Deep Learning-Based Motion Correction and Reconstruction for Accelerated Whole-Heart Joint T1/T2 Mapping
In conclusion, this study presents a promising method for efficient whole-heart myocardial tissue characterization. Specifically, the research highlights the potential of the multi-contrast end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data. The findings underscore its ability to compute T<sub>1</sub> and T<sub>2</sub> values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.
Authors
- Andrew Phair (ORCID: https://orcid.org/0000-0003-3527-0724)
- Claudia Prieto (ORCID: https://orcid.org/0000-0003-4602-2523)
- 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)
- Haikun Qi (ORCID: https://orcid.org/0000-0003-4709-5185)
- Carlos Velasco (ORCID: https://orcid.org/0000-0002-1226-3436)
Institutions
- Pontificia Universidad Católica de Chile (CL)
- King's College London (GB)
- ShanghaiTech University (CN)
Publication Details
- Journal
- Magnetic Resonance Imaging
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1016/j.mri.2025.110396
- Citations
- 3
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 4.08