Guo Rui, Wang Jiale, Miao Ying, Zhang Xinyu, Xue Song, Zhang Yu, Shi Kuangyu, Li Biao, Zheng Guoyan
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, Shanxi, China.
Eur Radiol. 2025 Mar;35(3):1133-1145. doi: 10.1007/s00330-024-11225-1. Epub 2024 Nov 28.
Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.
This retrospective study was performed on 456 participants respectively scanned by three different PET scanners with two different tracers. A DL method called spatially aware noise reduction network (SANR) was proposed to recover 3D full-dose (FD) PET volumes from LD data. The performance of SANR was compared with a 2D DL method taking regular FD PET volumes as the reference. Wilcoxon signed-rank test was conducted to compare the image quality metrics across different DL denoising methods. For clinical evaluation, two nuclear medicine physicians examined the recovered FD PET volumes using a 5-point grading scheme (5 = excellent) and gave a binary decision (negative or positive) for diagnostic quality assessment.
Statistically significant differences (p < 0.05) were found in terms of image quality metrics when SANR was compared with the 2D DL method. For clinical evaluation, SANR achieved a lesion detection accuracy of 95.3% (95% CI: 90.1%, 100%), while the reference full-dose PET volumes obtained a lesion detection accuracy of 98.4% (95% CI: 95.4%, 100%). In Alzheimer's disease diagnosis, both the reference FD PET volumes and the FD PET volumes recovered by SANR exhibited the same accuracy.
Compared with reference FD PET, LD PET denoised by the proposed approach significantly reduced radiotracer dosage and showed noninferior diagnostic performance in brain lesion detection and Alzheimer's disease diagnosis.
Question The current trend in PET imaging is to reduce injected dosage, which leads to low-quality PET images and reduces diagnostic efficacy. Findings The proposed deep learning method could recover diagnostic quality PET images from data acquired with a markedly reduced radiotracer dosage. Clinical relevance The proposed method would enhance the utility of PET scanning at lower radiotracer dosage and inform future workflows for brain lesion detection and Alzheimer's disease diagnosis, especially for those patients who need multiple examinations.
低剂量（LD）PET成像会导致图像质量下降和诊断效能降低。我们提出一种深度学习（DL）方法，在保持诊断质量的同时减少PET研究中的放射性示踪剂剂量。
本回顾性研究对456名参与者进行，他们分别使用两种不同的示踪剂通过三种不同的PET扫描仪进行扫描。提出了一种名为空间感知降噪网络（SANR）的DL方法，用于从LD数据中恢复3D全剂量（FD）PET容积。将SANR的性能与以常规FD PET容积为参考的2D DL方法进行比较。采用Wilcoxon符号秩检验比较不同DL去噪方法的图像质量指标。对于临床评估，两名核医学医师使用5分制评分方案（5分=优秀）检查恢复后的FD PET容积，并给出二元诊断质量评估结果（阴性或阳性）。
将SANR与2D DL方法比较时，在图像质量指标方面发现了具有统计学意义的差异（p < 0.05）。对于临床评估，SANR实现了95.3%的病变检测准确率（95%CI：90.1%，100%），而参考全剂量PET容积的病变检测准确率为98.4%（95%CI：95.4%，100%）。在阿尔茨海默病诊断中，参考FD PET容积和通过SANR恢复的FD PET容积表现出相同的准确率。
与参考FD PET相比，采用所提方法去噪的LD PET显著降低了放射性示踪剂剂量，并且在脑病变检测和阿尔茨海默病诊断中显示出非劣效的诊断性能。
问题：PET成像的当前趋势是减少注射剂量，这会导致PET图像质量低下并降低诊断效能。发现：所提出的深度学习方法能够从以显著降低放射性示踪剂剂量获取的数据中恢复具有诊断质量的PET图像。临床意义：所提方法将提高低放射性示踪剂剂量下PET扫描的效用，并为未来脑病变检测和阿尔茨海默病诊断的工作流程提供参考，特别是对于那些需要多次检查的患者。
