Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging
These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.
Authors
- Oliver Howes (ORCID: https://orcid.org/0000-0002-2928-1972)
- Maria Antonietta Nettis (ORCID: https://orcid.org/0000-0002-5401-8391)
- Nicola Toschi (ORCID: https://orcid.org/0000-0003-1929-5833)
- Mattia Veronese (ORCID: https://orcid.org/0000-0003-3562-0683)
- Matteo Ferrante (ORCID: https://orcid.org/0000-0002-3481-0832)
- Alexander C. Whitehead (ORCID: https://orcid.org/0000-0002-9151-3295)
- Ludovica Brusaferri (ORCID: https://orcid.org/0000-0001-6475-2840)
- Marianna Inglese (ORCID: https://orcid.org/0000-0002-9154-8260)
- Valeria Mondelli (ORCID: https://orcid.org/0000-0001-8690-6839)
- Marco L. Loggia (ORCID: https://orcid.org/0000-0002-8026-5265)
- Federico Turkheimer (ORCID: https://orcid.org/0000-0002-3766-3815)
- Lucia Maccioni (ORCID: https://orcid.org/0000-0002-6564-2827)
Institutions
- University of Rome Tor Vergata (IT)
- University of Padua (IT)
- Wellcome Centre for Human Neuroimaging (GB)
- King's College London (GB)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- University College London (GB)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-08-22
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108375
- Citations
- 12
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.85
Funders
- H2020 Fast Track to Innovation
- HORIZON EUROPE European Innovation Council