MRI recovery with self-calibrated denoisers without fully-sampled data
We present a self-supervised image reconstruction method and validate it in both static and dynamic MRI applications. These developments can benefit MRI applications where the availability of fully sampled training data is limited.
Authors
- Muhammad Shafique (ORCID: https://orcid.org/0000-0003-0020-9894)
- Philip Schniter (ORCID: https://orcid.org/0000-0003-0939-7545)
- Rizwan Ahmad (ORCID: https://orcid.org/0000-0002-5917-3788)
- Sizhuo Liu (ORCID: https://orcid.org/0000-0002-3248-0737)
Institutions
- COMSATS University Islamabad (PK)
- The Ohio State University (US)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1007/s10334-024-01207-1
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Institute of Biomedical Imaging and Bioengineering