Quantification of myocardial oxygen extraction fraction on noncontrast MRI enabled by deep learning
The new deep learning-enabled CMR approach allows noncontrast quantification of mOEF and MBV with good to excellent reproducibility. This technique could provide an objective contrast-free means to assess and serially measure hypoxia-relief effects of therapeutic interventional strategies to save viable myocardial tissues.
Authors
- Nhan Nguyen (ORCID: https://orcid.org/0000-0001-9079-8279)
- Qi Huang (ORCID: https://orcid.org/0000-0002-0847-6009)
- 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)
- Yan Yan (ORCID: https://orcid.org/0000-0002-4618-7654)
- Yang Yang (ORCID: https://orcid.org/0000-0001-6088-5435)
- Ran Li (ORCID: https://orcid.org/0000-0001-8647-1580)
- Keyan Wang (ORCID: https://orcid.org/0000-0001-8866-006X)
- Jingliang Cheng (ORCID: https://orcid.org/0000-0003-4223-5011)
- Pamela K. Woodard
Institutions
- University of California, San Francisco (US)
- University of California System (US)
- Mallinckrodt (United States) (US)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Radiology Advances
- Published
- 2024-10-26
- DOI
- https://doi.org/10.1093/radadv/umae026
- Citations
- 2
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- American Heart Association
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health