To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (SPET) images from low-dose PET (LPET). However, the success of current fully-supervised approaches relies on abundant paired LPET and SPET images, which are often unavailable in clinic. Moreover, these methods often mix the dose-invariant content with dose level-related dose-specific details during reconstruction, resulting in distorted images. To alleviate these problems, in this paper, we propose a two-stage Semi-Supervised SPET reconstruction framework, namely S3PET, to accommodate the training of abundant unpaired and limited paired SPET and LPET images. Our S3PET involves an un-supervised pre-training stage (Stage I) to extract representations from unpaired images, and a supervised dose-aware reconstruction stage (Stage II) to achieve LPET-to-SPET reconstruction by transferring the dose-specific knowledge between paired images. Specifically, in stage I, two independent dose-specific masked autoencoders (DsMAEs) are adopted to comprehensively understand the unpaired SPET and LPET images. Then, in Stage II, the pre-trained DsMAEs are further finetuned using paired images. To prevent distortions in both content and details, we introduce two elaborate modules, i.e., a dose knowledge decouple module to disentangle the respective dose-specific and dose-invariant knowledge of LPET and SPET, and a dose-specific knowledge learning module to transfer the dose-specific information from SPET to LPET, thereby achieving high-quality SPET reconstruction from LPET images. Experiments on two datasets demonstrate that our S3PET achieves state-of-the-art performance quantitatively and qualitatively.

翻译：为在降低放射性示踪剂剂量的同时获取高质量正电子发射断层扫描（PET）图像，大量研究致力于从低剂量PET（LPET）重建标准剂量PET（SPET）图像。然而，当前全监督方法的成功依赖于充足的成对LPET与SPET图像，这在临床中往往难以获取。此外，这些方法在重建过程中常将剂量无关内容与剂量水平相关的剂量特异性细节相混淆，导致图像失真。为缓解这些问题，本文提出一种两阶段半监督SPET重建框架S3PET，以适配大量非成对及有限成对SPET与LPET图像的训练。我们的S3PET包含无监督预训练阶段（阶段Ⅰ）从非成对图像中提取表征，以及有监督剂量感知重建阶段（阶段Ⅱ）通过迁移成对图像间的剂量特异性知识实现LPET到SPET的重建。具体而言，在阶段Ⅰ采用两个独立的剂量特异性掩码自编码器（DsMAE）全面理解非成对SPET与LPET图像；在阶段Ⅱ，使用成对图像对预训练的DsMAE进行微调。为防止内容与细节失真，我们引入两个精心设计的模块：剂量知识解耦模块用于分离LPET与SPET各自的剂量特异性及剂量不变知识；剂量特异性知识学习模块将SPET的剂量特异性信息迁移至LPET，从而实现从LPET图像的高质量SPET重建。在两个数据集上的实验表明，S3PET在定量与定性评估中均达到最先进性能。