Wang Taisong, Qiao Wenli, Wang Ying, Wang Jingyi, Lv Yang, Dong Yun, Qian Zheng, Xing Yan, Zhao Jinhua
Department of Nuclear Medicine, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, No. 100 Haining Road, Shanghai, 200080, People's Republic of China.
United Imaging Healthcare, Shanghai, People's Republic of China.
EJNMMI Phys. 2022 Nov 22;9(1):82. doi: 10.1186/s40658-022-00508-5.
To validate a total-body PET-guided deep progressive learning reconstruction method (DPR) for low-dose F-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 F-FDG by up to 2/3 in a real-world deployment while maintaining image quality.
验证一种用于低剂量F-FDG PET成像的全身PET引导深度渐进学习重建方法（DPR）。
将回顾性研究（n = 26）中的列表模式数据重新分箱为短时长扫描，并使用DPR进行重建。将DPR图像中病变的标准摄取值（SUV）、肿瘤与肝脏比值（TLR）以及肝脏中的变异系数（COV）与参考图像（具有全时长数据的OSEM图像）进行比较。在前瞻性研究中，根据回顾性结果，另外41名患者注射了1/3剂量的放射性示踪剂。生成DPR图像（DPR_1/3(p)）并与参考图像（具有延长采集时间的OSEM图像）进行比较。在三个选定器官（肝脏、血池和肌肉）中评估SUV和COV。对病变SUV和TLR进行定量分析，此外还对小病变（直径≤10 mm）进行分析。另外，从对比度、噪声和诊断置信度等方面进行5分制李克特量表视觉分析。
在回顾性研究中，时长为三分之一的DPR能够保持与参考图像相当的图像质量。在前瞻性研究中，在所有选定器官中观察到SUV之间具有良好的一致性。定量结果表明，DPR_1/3(p)组与参考组之间的COV没有显著差异，而视觉分析表明图像对比度、噪声和诊断置信度没有显著差异。与参考组相比，DPR_1/3(p)组的病变SUV和TLR显著提高，即使对于小病变也是如此。
所提出的DPR方法在实际应用中可将F-FDG的给药剂量降低多达2/3，同时保持图像质量。
