Model‐based self‐supervised learning for quantitative assessment of myocardial oxygen extraction fraction and myocardial blood volume
This work has demonstrated the initial feasibility of generating mOEF and MBV maps simultaneously by a model-driven, self-supervised learning method.
Authors
- Ran Li
- Nhan Nguyen (ORCID: https://orcid.org/0000-0001-9079-8279)
- Pamela K. Woodard (ORCID: https://orcid.org/0000-0001-9012-0812)
- Jingliang Cheng (ORCID: https://orcid.org/0000-0002-6996-329X)
- Cihat Eldeniz (ORCID: https://orcid.org/0000-0002-4457-0916)
- Linda R. Peterson (ORCID: https://orcid.org/0000-0001-8766-4552)
- Thomas H. Schindler (ORCID: https://orcid.org/0000-0002-2141-7716)
- Jie Zheng (ORCID: https://orcid.org/0000-0002-2310-2996)
- Haoteng Tang (ORCID: https://orcid.org/0000-0003-0323-1755)
- Yan Yan (ORCID: https://orcid.org/0000-0002-4618-7654)
- Yang Yang (ORCID: https://orcid.org/0000-0001-6088-5435)
- Keyan Wang (ORCID: https://orcid.org/0000-0001-8866-006X)
- Qi Huang (ORCID: https://orcid.org/0000-0001-6545-2487)
Institutions
- University of California, San Francisco (US)
- The University of Texas Rio Grande Valley (US)
- University of California System (US)
- Mallinckrodt (United States) (US)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1002/mrm.30555
- Citations
- 4
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- American Heart Association
- Georgia Clinical and Translational Science Alliance