An unsupervised method for MRI recovery: deep image prior with structured sparsity
An unsupervised image reconstruction method is presented and validated on simulated and measured data. These developments can benefit applications where acquiring fully sampled data is challenging.
Authors
- Katarzyna Gil (ORCID: https://orcid.org/0000-0003-1034-8337)
- Rizwan Ahmad (ORCID: https://orcid.org/0000-0002-5917-3788)
- Karolina M. Zaręba (ORCID: https://orcid.org/0000-0002-4891-4106)
- Yingmin Liu (ORCID: https://orcid.org/0000-0001-5886-8636)
- Muhammad Ahmad Sultan (ORCID: https://orcid.org/0009-0007-0185-7400)
- Chong Chen (ORCID: https://orcid.org/0000-0002-4965-5564)
Institutions
- The Ohio State University Wexner Medical Center (US)
- The Ohio State University (US)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1007/s10334-025-01257-z
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Siemens Healthineers
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Institute of Biomedical Imaging and Bioengineering