Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning
We conclude that it is feasible to generate robust SV2A PET images with promising accuracy from MRI via a data-driven approach.
Authors
- Gustavo A. Angarita (ORCID: https://orcid.org/0000-0001-5568-9878)
- Xueqi Guo (ORCID: https://orcid.org/0000-0002-0416-2811)
- Richard E. Carson (ORCID: https://orcid.org/0000-0002-9338-7966)
- Patrick D. Worhunsky (ORCID: https://orcid.org/0000-0001-9629-1428)
- Kelly Cosgrove (ORCID: https://orcid.org/0000-0003-1351-9576)
- Xiongchao Chen (ORCID: https://orcid.org/0000-0003-4112-8492)
- Takuya Toyonaga (ORCID: https://orcid.org/0000-0002-0369-8294)
- Ryan S. O’Dell (ORCID: https://orcid.org/0000-0001-7673-6661)
- Rajiv Radhakrishnan (ORCID: https://orcid.org/0000-0002-8220-655X)
- Irina Esterlis (ORCID: https://orcid.org/0000-0001-6293-1458)
- Christopher H. van Dyck (ORCID: https://orcid.org/0000-0003-1779-097X)
- Deepak Cyril D’Souza (ORCID: https://orcid.org/0000-0003-3141-1462)
- Adam P. Mecca (ORCID: https://orcid.org/0000-0003-1982-5443)
- David Matuskey (ORCID: https://orcid.org/0000-0001-7508-6572)
- Qiong Liu (ORCID: https://orcid.org/0000-0001-8153-233X)
- Heng Sun
- Chi Liu (ORCID: https://orcid.org/0000-0002-7007-1037)
- Xinyuan Zheng (ORCID: https://orcid.org/0009-0003-2808-5907)
- Jiazhen Zhang (ORCID: https://orcid.org/0000-0001-6129-1655)
Institutions
- Yale University (US)
Publication Details
- Journal
- EJNMMI Physics
- Published
- 2025-03-31
- DOI
- https://doi.org/10.1186/s40658-025-00744-5
- Citations
- 4
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Institutes of Health
- National Institute on Aging
- National Institute on Drug Abuse