SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting
Our results demonstrate that the proposed SuperMRF can achieve rapid, robust reconstruction with reduced frames in addition to k-space undersampling, outperforming the conventional and state-of-the-art reconstruction methods in simulation and providing comparable results to low rank reconstruction in prospective real-world subjects.
Authors
- Alan Li (ORCID: https://orcid.org/0000-0002-2037-6262)
- Brendan Eck (ORCID: https://orcid.org/0000-0003-0971-4432)
- Ruiying Liu (ORCID: https://orcid.org/0000-0001-7851-4253)
- Leslie Ying (ORCID: https://orcid.org/0000-0001-9801-1362)
- Peizhou Huang (ORCID: https://orcid.org/0000-0003-2503-2335)
- Mingrui Yang (ORCID: https://orcid.org/0000-0002-8902-6316)
- Hongyu Li (ORCID: https://orcid.org/0000-0002-4588-9929)
- Dong Liang (ORCID: https://orcid.org/0000-0001-6257-0875)
- Jeehun Kim
Institutions
- Cleveland Clinic (US)
- Cleveland Clinic Lerner College of Medicine (US)
- University at Buffalo, State University of New York (US)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.21037/qims-23-1819
- Primary Topic
- Nuclear Physics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00