Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers
This study demonstrated that the DLToF models are suitable for both FDG and non-FDG tracers and could be utilized for digital BGO PET/CT scanners to provide an image quality and lesion detectability comparable and close to ToF.
Authors
- Andrei Iagaru (ORCID: https://orcid.org/0000-0003-0839-5329)
- Robert Johnsen
- Floris Jansen (ORCID: https://orcid.org/0000-0002-8437-4960)
- Theodore Colwell
- Fotis A. Kotasidis
- Martin William Huellner (ORCID: https://orcid.org/0000-0002-4849-3292)
- Daniel R. McGowan (ORCID: https://orcid.org/0000-0002-6880-5687)
- Patrick Fielding (ORCID: https://orcid.org/0000-0002-5914-3271)
- Scott D. Wollenweber (ORCID: https://orcid.org/0000-0001-6105-6831)
- Abolfazl Mehranian (ORCID: https://orcid.org/0000-0003-4584-4453)
- Kevin M. Bradley (ORCID: https://orcid.org/0000-0003-1911-3382)
- Meghi Dedja
Institutions
- University Hospital of Wales (GB)
- University of Oxford (GB)
- University Hospital of Zurich (CH)
- Stanford Medicine (US)
- Centre for Sustainable Healthcare (GB)
- Oxford University Hospitals NHS Trust (GB)
- Cardiff University (GB)
- Stanford University (US)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1007/s00259-025-07119-z
- Citations
- 4
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Cancer Research UK