Repeatability‐encouraging self‐supervised learning reconstruction for quantitative <scp>MRI</scp>
Without the need for labeled training data, the proposed SSL method demonstrated superior repeatability compared with supervised learning without sacrificing sharpness, and reduced reconstruction time versus iterative methods.
Authors
- Yibin Xie (ORCID: https://orcid.org/0000-0002-0333-567X)
- Zihao Chen (ORCID: https://orcid.org/0000-0001-7838-4989)
- Zheyuan Hu (ORCID: https://orcid.org/0000-0002-0276-8254)
- Anthony Christodoulou (ORCID: https://orcid.org/0000-0002-9334-8684)
- Debiao Li
Institutions
- Cedars-Sinai Medical Center (US)
- University of California, Los Angeles (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-02-27
- DOI
- https://doi.org/10.1002/mrm.30478
- Citations
- 4
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Institutes of Health