To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods merely learn the mapping between single-dose-level LPET and SPET images, but omit the dose disparity of LPET images in clinical scenarios. In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase. Specifically, the pre-training phase is devised to explore both fine-grained discriminative features and effective semantic representation. The SPET prediction phase adopts a coarse prediction network utilizing pre-learned dose level prior to generate preliminary result, and a refinement network to precisely preserve the details. Experiments on MICCAI 2022 Ultra-low Dose PET Imaging Challenge Dataset have demonstrated the superiority of our method.

翻译：为在获得高质量正电子发射断层扫描（PET）图像的同时最大限度减少辐射暴露，研究者设计了多种方法，从低剂量PET（LPET）图像重建标准剂量PET（SPET）图像。然而，现有方法大多仅学习单剂量水平LPET与SPET图像之间的映射关系，忽略了临床场景中LPET图像的剂量差异。本文针对多剂量水平LPET图像重建高质量SPET图像的问题，提出了一种具有剂量水平感知能力的新型两阶段多剂量水平PET重建算法，包括预训练阶段和SPET预测阶段。具体而言，预训练阶段旨在探索细粒度判别特征与有效语义表征；SPET预测阶段采用粗预测网络（利用预学习的剂量水平先验生成初步结果）和精修网络（精确保留细节）。在MICCAI 2022超低剂量PET成像挑战数据集上的实验证明了本方法的优越性。