Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18F-FDG PET Scans
We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18 F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts' consensus in IQA studies.
Authors
- Mehdi Amini (ORCID: https://orcid.org/0000-0002-4370-680X)
- Yazdan Salimi (ORCID: https://orcid.org/0000-0002-1233-9576)
- Isaac Shiri (ORCID: https://orcid.org/0000-0002-5735-0736)
- Habib Zaidi (ORCID: https://orcid.org/0000-0001-7559-5297)
- Arman Rahmim (ORCID: https://orcid.org/0000-0002-9980-2403)
- Ghasem Hajianfar (ORCID: https://orcid.org/0000-0001-5359-2407)
- Ismini Mainta (ORCID: https://orcid.org/0000-0001-5296-0116)
- Elsa Hervier
- Amirhossein Sanaat (ORCID: https://orcid.org/0000-0001-8437-2060)
Institutions
- University Medical Center Groningen (NL)
- University of British Columbia (CA)
- University of Groningen (NL)
- University of Southern Denmark (DK)
- University Hospital of Geneva (CH)
Publication Details
- Journal
- Clinical Nuclear Medicine
- Published
- 2024-10-21
- DOI
- https://doi.org/10.1097/rlu.0000000000005526
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Hôpitaux Universitaires de Genève