  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.
