18F-FDG dose reduction using deep learning-based PET reconstruction
Abstract Background A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It may reduce the administered dose of 18F-FDG and minimize radiation exposure while maintaining diagnostic quality. This retrospective study ev...
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SpringerOpen
2025-07-01
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| Series: | EJNMMI Research |
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| Online Access: | https://doi.org/10.1186/s13550-025-01269-9 |
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| author | Ryuji Akita Komei Takauchi Mana Ishibashi Shota Kondo Shogo Ono Kazushi Yokomachi Yusuke Ochi Masao Kiguchi Hidenori Mitani Yuko Nakamura Kazuo Awai |
| author_facet | Ryuji Akita Komei Takauchi Mana Ishibashi Shota Kondo Shogo Ono Kazushi Yokomachi Yusuke Ochi Masao Kiguchi Hidenori Mitani Yuko Nakamura Kazuo Awai |
| author_sort | Ryuji Akita |
| collection | DOAJ |
| description | Abstract Background A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It may reduce the administered dose of 18F-FDG and minimize radiation exposure while maintaining diagnostic quality. This retrospective study evaluated whether the injected 18F-FDG dose could be reduced by applying DLR to PET images. To this aim, we compared the quantitative image quality metrics and the false-positive rate between DLR with a reduced 18F-FDG dose and Ordered Subsets Expectation Maximization (OSEM) with a standard dose. Results This study included 90 oncology patients who underwent 18F-FDG PET/CT. They were divided into 3 groups (30 patients each): group A (18F-FDG dose per body weight [BW]: 2.00—2.99 MBq/kg; PET image reconstruction: DLR), group B (3.00–3.99 MBq/kg; DLR), and group C (standard dose group; 4.00—4.99 MBq/kg; OSEM). The evaluation was performed using the signal-to-noise ratio (SNR), target-to-background ratio (TBR), and false-positive rate. DLR yielded significantly higher SNRs in groups A and B than group C (p < 0.001). There was no significant difference in the TBR between groups A and C, and between groups B and C (p = 0.983 and 0.605, respectively). In group B, more than 80% of patients weighing less than 75 kg had at most one false positive result. In contrast, in group B patients weighing 75 kg or more, as well as in group A, less than 80% of patients had at most one false-positives. Conclusions Our findings suggest that the injected 18F-FDG dose can be reduced to 3.0 MBq/kg in patients weighing less than 75 kg by applying DLR. Compared to the recommended dose in the European Association of Nuclear Medicine (EANM) guidelines for 90 s per bed position (4.7 MBq/kg), this represents a dose reduction of 36%. Further optimization of DLR algorithms is required to maintain comparable diagnostic accuracy in patients weighing 75 kg or more. |
| format | Article |
| id | doaj-art-ff3ffd66642e4a95bc3714142c9b45e7 |
| institution | Kabale University |
| issn | 2191-219X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Research |
| spelling | doaj-art-ff3ffd66642e4a95bc3714142c9b45e72025-08-20T04:01:41ZengSpringerOpenEJNMMI Research2191-219X2025-07-011511910.1186/s13550-025-01269-918F-FDG dose reduction using deep learning-based PET reconstructionRyuji Akita0Komei Takauchi1Mana Ishibashi2Shota Kondo3Shogo Ono4Kazushi Yokomachi5Yusuke Ochi6Masao Kiguchi7Hidenori Mitani8Yuko Nakamura9Kazuo Awai10Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima UniversityDepartment of Radiology, Hiroshima University HospitalCenter for Radiation Disaster Medical Science, Research Institute for Radiation Biology and Medicine, Hiroshima UniversityDepartment of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima UniversityCT Systems Development Department, Canon Medical Systems CorporationDepartment of Radiology, Hiroshima University HospitalDepartment of Radiology, Hiroshima University HospitalDepartment of Radiology, Hiroshima University HospitalDepartment of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima UniversityDepartment of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima UniversityDepartment of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima UniversityAbstract Background A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It may reduce the administered dose of 18F-FDG and minimize radiation exposure while maintaining diagnostic quality. This retrospective study evaluated whether the injected 18F-FDG dose could be reduced by applying DLR to PET images. To this aim, we compared the quantitative image quality metrics and the false-positive rate between DLR with a reduced 18F-FDG dose and Ordered Subsets Expectation Maximization (OSEM) with a standard dose. Results This study included 90 oncology patients who underwent 18F-FDG PET/CT. They were divided into 3 groups (30 patients each): group A (18F-FDG dose per body weight [BW]: 2.00—2.99 MBq/kg; PET image reconstruction: DLR), group B (3.00–3.99 MBq/kg; DLR), and group C (standard dose group; 4.00—4.99 MBq/kg; OSEM). The evaluation was performed using the signal-to-noise ratio (SNR), target-to-background ratio (TBR), and false-positive rate. DLR yielded significantly higher SNRs in groups A and B than group C (p < 0.001). There was no significant difference in the TBR between groups A and C, and between groups B and C (p = 0.983 and 0.605, respectively). In group B, more than 80% of patients weighing less than 75 kg had at most one false positive result. In contrast, in group B patients weighing 75 kg or more, as well as in group A, less than 80% of patients had at most one false-positives. Conclusions Our findings suggest that the injected 18F-FDG dose can be reduced to 3.0 MBq/kg in patients weighing less than 75 kg by applying DLR. Compared to the recommended dose in the European Association of Nuclear Medicine (EANM) guidelines for 90 s per bed position (4.7 MBq/kg), this represents a dose reduction of 36%. Further optimization of DLR algorithms is required to maintain comparable diagnostic accuracy in patients weighing 75 kg or more.https://doi.org/10.1186/s13550-025-01269-918F-FDGPET/CTDose reductionDeep learning-based reconstructionImage quality |
| spellingShingle | Ryuji Akita Komei Takauchi Mana Ishibashi Shota Kondo Shogo Ono Kazushi Yokomachi Yusuke Ochi Masao Kiguchi Hidenori Mitani Yuko Nakamura Kazuo Awai 18F-FDG dose reduction using deep learning-based PET reconstruction EJNMMI Research 18F-FDG PET/CT Dose reduction Deep learning-based reconstruction Image quality |
| title | 18F-FDG dose reduction using deep learning-based PET reconstruction |
| title_full | 18F-FDG dose reduction using deep learning-based PET reconstruction |
| title_fullStr | 18F-FDG dose reduction using deep learning-based PET reconstruction |
| title_full_unstemmed | 18F-FDG dose reduction using deep learning-based PET reconstruction |
| title_short | 18F-FDG dose reduction using deep learning-based PET reconstruction |
| title_sort | 18f fdg dose reduction using deep learning based pet reconstruction |
| topic | 18F-FDG PET/CT Dose reduction Deep learning-based reconstruction Image quality |
| url | https://doi.org/10.1186/s13550-025-01269-9 |
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