Deep learning-guided attenuation and scatter correction of 99mTc-MAA SPECT images: towards quantitative analysis in 90Y-SIRT
Multiple models were developed for key SPECT quantification tasks, with potential value in clinical setting lacking reliable CT data or sufficient computational resources for Monte Carlo simulations. The models look promising for potential clinical translation and integration into commercial reconstruction software.
Authors
- Yazdan Salimi (ORCID: https://orcid.org/0000-0002-1233-9576)
- Valentina Garibotto (ORCID: https://orcid.org/0000-0003-2422-698X)
- Ghasem Hajianfar (ORCID: https://orcid.org/0000-0001-5359-2407)
- Ismini Mainta (ORCID: https://orcid.org/0000-0001-5296-0116)
- Nicola Bianchetto Wolf (ORCID: https://orcid.org/0009-0008-4447-4802)
- Habib Zaidi
- Zahra Mansouri
Institutions
- Obuda University (HU)
- University of Geneva (CH)
- University Medical Center Groningen (NL)
- University of Southern Denmark (DK)
- University Hospital of Geneva (CH)
- Centre d'Imagerie BioMedicale (CH)
Publication Details
- Journal
- Annals of Nuclear Medicine
- Published
- 2026-01-05
- DOI
- https://doi.org/10.1007/s12149-025-02152-2
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung