  18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging
usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic
results, which raises concerns about the potential health risks of radiation
exposure, especially for pediatric patients. Reconstructing the low-dose PET
(L-PET) images to the high-quality full-dose PET (F-PET) ones is an effective
way that both reduces the radiation exposure and remains diagnostic accuracy.
In this paper, we propose a resource-efficient deep learning framework for
L-PET reconstruction and analysis, referred to as transGAN-SDAM, to generate
F-PET from corresponding L-PET, and quantify the standard uptake value ratios
(SUVRs) of these generated F-PET at whole brain. The transGAN-SDAM consists of
two modules: a transformer-encoded Generative Adversarial Network (transGAN)
and a Spatial Deformable Aggregation Module (SDAM). The transGAN generates
higher quality F-PET images, and then the SDAM integrates the spatial
information of a sequence of generated F-PET slices to synthesize whole-brain
F-PET images. Experimental results demonstrate the superiority and rationality
of our approach.
