  Positron emission tomography (PET) is the most sensitive molecular imaging
modality routinely applied in our modern healthcare. High radioactivity caused
by the injected tracer dose is a major concern in PET imaging and limits its
clinical applications. However, reducing the dose leads to inadequate image
quality for diagnostic practice. Motivated by the need to produce high quality
images with minimum low-dose, Convolutional Neural Networks (CNNs) based
methods have been developed for high quality PET synthesis from its low-dose
counterparts. Previous CNNs-based studies usually directly map low-dose PET
into features space without consideration of different dose reduction level. In
this study, a novel approach named CG-3DSRGAN (Classification-Guided Generative
Adversarial Network with Super Resolution Refinement) is presented.
Specifically, a multi-tasking coarse generator, guided by a classification
head, allows for a more comprehensive understanding of the noise-level features
present in the low-dose data, resulting in improved image synthesis. Moreover,
to recover spatial details of standard PET, an auxiliary super resolution
network - Contextual-Net - is proposed as a second-stage training to narrow the
gap between coarse prediction and standard PET. We compared our method to the
state-of-the-art methods on whole-body PET with different dose reduction
factors (DRFs). Experiments demonstrate our method can outperform others on all
DRF.
