Enhancing 18F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm
DPL consistently provided superior image quality and lesion diagnostic performance compared to OSEM across all BMI categories in <sup>18</sup>F-FDG PET/CT scans. Therefore, we recommend using the DPL algorithm for <sup>18</sup>F-FDG PET/CT image reconstruction in all BMI patients.
Authors
- Ming Qi (ORCID: https://orcid.org/0000-0001-9880-5349)
- Zhihao Chen (ORCID: https://orcid.org/0000-0002-8201-0960)
- 杨阳 YANG Yang (ORCID: https://orcid.org/0000-0002-4528-7020)
- Shaoli Song (ORCID: https://orcid.org/0000-0003-2544-7522)
- Jianping Zhang (ORCID: https://orcid.org/0000-0002-1929-792X)
- Wen Chen (ORCID: https://orcid.org/0000-0001-7017-6990)
- Fei Liu (ORCID: https://orcid.org/0000-0002-9814-4226)
Institutions
- Shanghai Medical College of Fudan University (CN)
- Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1186/s40644-025-00877-x
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shanghai Municipal Education Commission