Ultrafast Multi-tracer Total-body PET Imaging Using a Transformer-Based Deep Learning Model
The proposed Model 1 and Model 2 effectively enhance image quality of multi-tracer total-body PET scans with ultrafast acquisition times. The predicted PET images demonstrate comparable performance in terms of image quality and lesion detectability.
Authors
- Yazdan Salimi (ORCID: https://orcid.org/0000-0002-1233-9576)
- Habib Zaidi (ORCID: https://orcid.org/0000-0001-7559-5297)
- Charles-Edouard Decorads (ORCID: https://orcid.org/0009-0006-9055-5801)
- Lijun Lu (ORCID: https://orcid.org/0000-0002-7001-4892)
- Hubing Wu (ORCID: https://orcid.org/0000-0002-7546-5430)
- Yanchao Huang
- Wenxiang Yi
- Hao Sun
- Amirhossein Sanaat (ORCID: https://orcid.org/0000-0001-8437-2060)
- Inès Castarède
Institutions
- University Medical Center Groningen (NL)
- University Hospital of Geneva (CH)
- Southern Medical University (CN)
Publication Details
- Journal
- Academic Radiology
- Published
- 2025-08-30
- DOI
- https://doi.org/10.1016/j.acra.2025.08.015
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- National Natural Science Foundation of China