  Reducing the injected dose would result in quality degradation and loss of
information in PET imaging. To address this issue, deep learning methods have
been introduced to predict standard PET images (S-PET) from the corresponding
low-dose versions (L-PET). The existing deep learning-based denoising methods
solely rely on a single dose level of PET images to predict the S-PET images.
In this work, we proposed to exploit the prior knowledge in the form of
multiple low-dose levels of PET images (in addition to the target low-dose
level) to estimate the S-PET images.
