| Wang Yuan,Yang Xueqian,Hao Zhixin,et al.Comparing the effects of the deep progressive reconstruction and ordered subsets expectation maximization algorithms on the quality of the 18F-FDG PET/MR images of liver and pancreas[J].Chinese Journal of Radiological Medicine and Protection,2026,46(8):795-803 |
| Comparing the effects of the deep progressive reconstruction and ordered subsets expectation maximization algorithms on the quality of the 18F-FDG PET/MR images of liver and pancreas |
| Received:February 08, 2026 |
| DOI：10.3760/cma.j.cn112271-20260208-00045 |
| KeyWords:18F-FDG PET/MR|Deep progressive reconstruction|Equivalent low-dose simulation|Liver|Pancreas |
| FundProject:国家自然科学基金(U24A20758);北京市自然科学基金(L242062);国家重点研发计划(2024YFC2419400);中国医学科学院医学与健康科技创新工程项目(CIFMS-2024-I2M-ZD-001,CIFMS-2023-I2M-2-002,CIFMS-2021-I2M-1-002,CIFMS-2021-I2M-1-003,CIFMS-2021-I2M-1-025);中央高水平医院临床科研专项项目(2022-PUMCH-D-001,2022-PUMCH-D-002) |
| Author Name | Affiliation | E-mail | | Wang Yuan | Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Disease Research, Beijing 100730, China | | | Yang Xueqian | Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Disease Research, Beijing 100730, China | | | Hao Zhixin | Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Disease Research, Beijing 100730, China | | | Li Enhui | Beijing United Imaging Healthcare Technology Development Co., Ltd., Beijing 100094, China | | | Su Xiaofan | Beijing United Imaging Healthcare Technology Development Co., Ltd., Beijing 100094, China | | | Yang Yang | Beijing United Imaging Healthcare Technology Development Co., Ltd., Beijing 100094, China | | | Huo Li | Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Disease Research, Beijing 100730, China | | | Xing Haiqun | Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Disease Research, Beijing 100730, China | 15801255152@163.com |
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| Abstract:: |
| Objective To compare the effects of deep progressive reconstruction (DPR) and ordered subsets expectation maximization (OSEM) algorithms on the quality of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images of liver and pancreas acquired using an integrated positron emission tomography/magnetic resonance (PET/MR) imaging system, and to assess the potential value of DPR in improving image quality, suppressing noise, and reconstructing equivalent low-dose PET scans. Methods A retrospective study was conducted on 23 patients who underwent abdominal 18F-FDG PET/MRI scans. The resulting PET images were reconstructed using the standard OSEM algorithm and the DPR algorithms of levels 1-3. Then, equivalent low-dose conditions corresponding to 1/2, 1/3, and 1/4 of the standard dose were simulated by reducing acquisition time. The PET images were subjected to qualitative and quantitative quality evaluations. Regarding qualitative evaluation, the images were scored using the 5-point Likert scale under double-blind conditions by two experienced nuclear medicine physicians independently. For quantitative evaluation, volumes of interest (VOIs) were delineated in the liver and pancreas zones, and their signal-to-noise ratio (SNR) and noise index were calculated. Results Qualitative (subjective) evaluation indicated that compared to those reconstructed using OSEM, images reconstructed using the DPR algorithms at all levels exhibited significantly higher scores in terms of overall image quality, image sharpness, artifact suppression, noise suppression, and liver uniformity, with high inter-observer consistency. Quantitative evaluation revealed that compared to OSEM-reconstructed images, DPR-reconstructed images showed higher SNR and lower noise in the liver and pancreas VOIs. Among the DPR algorithm of three levels, level-1 DPR better preserved the details of tissues and structures in images while effectively suppressing noise, achieving a good balance between image quality and noise control. The equivalent low-dose simulation experiments revealed that DPR-reconstructed images with acquisition time reduced by 1/2, equivalent to images acquired under half doses, still showed significantly higher quality than images reconstructed using OSEM under full acquisition time. Conclusions Compared to the conventional OSEM algorithm, DPR significantly improves the quality of 18F-FDG PET/MR images of the liver and pancreas and maintains high imaging stability under reduced acquisition time or equivalent low-dose conditions. Among the DPR algorithms at all levels, the level-1 algorithm delivers the optimal overall performance, holding potential value in acting as a parameter for the clinical standardized reconstruction of abdominal PET/MR images. |
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