  Low resolution of positron emission tomography (PET) limits its diagnostic
performance. Deep learning has been successfully applied to achieve
super-resolution PET. However, commonly used supervised learning methods in
this context require many pairs of low- and high-resolution (LR and HR) PET
images. Although unsupervised learning utilizes unpaired images, the results
are not as good as that obtained with supervised deep learning. In this paper,
we propose a quasi-supervised learning method, which is a new type of
weakly-supervised learning methods, to recover HR PET images from LR
counterparts by leveraging similarity between unpaired LR and HR image patches.
Specifically, LR image patches are taken from a patient as inputs, while the
most similar HR patches from other patients are found as labels. The similarity
between the matched HR and LR patches serves as a prior for network
construction. Our proposed method can be implemented by designing a new network
or modifying an existing network. As an example in this study, we have modified
the cycle-consistent generative adversarial network (CycleGAN) for
super-resolution PET. Our numerical and experimental results qualitatively and
quantitatively show the merits of our method relative to the state-ofthe-art
methods. The code is publicly available at
https://github.com/PigYang-ops/CycleGAN-QSDL.
