  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.
