  Deep image prior (DIP) has been successfully applied to positron emission
tomography (PET) image restoration, enabling represent implicit prior using
only convolutional neural network architecture without training dataset,
whereas the general supervised approach requires massive low- and high-quality
PET image pairs. To answer the increased need for PET imaging with DIP, it is
indispensable to improve the performance of the underlying DIP itself. Here, we
propose a self-supervised pre-training model to improve the DIP-based PET image
denoising performance. Our proposed pre-training model acquires transferable
and generalizable visual representations from only unlabeled PET images by
restoring various degraded PET images in a self-supervised approach. We
evaluated the proposed method using clinical brain PET data with various
radioactive tracers ($^{18}$F-florbetapir, $^{11}$C-Pittsburgh compound-B,
$^{18}$F-fluoro-2-deoxy-D-glucose, and $^{15}$O-CO$_{2}$) acquired from
different PET scanners. The proposed method using the self-supervised
pre-training model achieved robust and state-of-the-art denoising performance
while retaining spatial details and quantification accuracy compared to other
unsupervised methods and pre-training model. These results highlight the
potential that the proposed method is particularly effective against rare
diseases and probes and helps reduce the scan time or the radiotracer dose
without affecting the patients.
