Semi-supervision for clinical contrast-weighted image synthesis from magnetic resonance fingerprinting
The proposed method enables semi-supervised learning for synthesis from MRF with an end-to-end, ultra-fast training data acquisition protocol that is easier to obtain across a large population in clinical settings.
Authors
- John M. Pauly (ORCID: https://orcid.org/0000-0001-5918-4172)
- Congyu Liao (ORCID: https://orcid.org/0000-0003-2270-276X)
- Cagan Alkan (ORCID: https://orcid.org/0000-0002-0241-0631)
- Shreyas Vasanawala (ORCID: https://orcid.org/0000-0002-1999-6595)
- Mahmut Yurt (ORCID: https://orcid.org/0000-0003-3280-4217)
- Xiaozhi Cao (ORCID: https://orcid.org/0000-0001-5095-648X)
- Zihan Zhou (ORCID: https://orcid.org/0000-0002-9034-0951)
- Ali Syed
- Tolga Cukur
- Kawin Setsompop
Institutions
- Bilkent University (TR)
- University of California, San Francisco (US)
- Stanford University (US)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2026-03-17
- DOI
- https://doi.org/10.1007/s10334-026-01338-7
- Primary Topic
- Generative Adversarial Networks and Image Synthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.00