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• W4309673644 abstract "To validate a total-body PET-guided deep progressive learning reconstruction method (DPR) for low-dose 18F-FDG PET imaging.List-mode data from the retrospective study (n = 26) were rebinned into short-duration scans and reconstructed with DPR. The standard uptake value (SUV) and tumor-to-liver ratio (TLR) in lesions and coefficient of variation (COV) in the liver in the DPR images were compared to the reference (OSEM images with full-duration data). In the prospective study, another 41 patients were injected with 1/3 of the activity based on the retrospective results. The DPR images (DPR_1/3(p)) were generated and compared with the reference (OSEM images with extended acquisition time). The SUV and COV were evaluated in three selected organs: liver, blood pool and muscle. Quantitative analyses were performed with lesion SUV and TLR, furthermore on small lesions (≤ 10 mm in diameter). Additionally, a 5-point Likert scale visual analysis was performed on the following perspectives: contrast, noise and diagnostic confidence.In the retrospective study, the DPR with one-third duration can maintain the image quality as the reference. In the prospective study, good agreement among the SUVs was observed in all selected organs. The quantitative results showed that there was no significant difference in COV between the DPR_1/3(p) group and the reference, while the visual analysis showed no significant differences in image contrast, noise and diagnostic confidence. The lesion SUVs and TLRs in the DPR_1/3(p) group were significantly enhanced compared with the reference, even for small lesions.The proposed DPR method can reduce the administered activity of 18F-FDG by up to 2/3 in a real-world deployment while maintaining image quality." @default.
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• W4309673644 date "2022-11-22" @default.
• W4309673644 modified "2023-10-16" @default.
• W4309673644 title "Deep progressive learning achieves whole-body low-dose 18F-FDG PET imaging" @default.
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• W4309673644 doi "https://doi.org/10.1186/s40658-022-00508-5" @default.
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