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.

翻译：深度图像先验（DIP）已成功应用于正电子发射断层扫描（PET）图像恢复，它仅利用卷积神经网络架构即可隐式表达先验信息，无需训练数据集，而传统监督方法则需要大量低质量与高质量PET图像对。为满足基于DIP的PET成像日益增长的需求，提升DIP本身的性能至关重要。本文提出一种自监督预训练模型，以改进基于DIP的PET图像去噪性能。该预训练模型通过自监督方式恢复多种退化PET图像，从无标注PET图像中获取可迁移且可泛化的视觉表征。我们采用来自不同PET扫描仪获取的多种放射性示踪剂（$^{18}$F-florbetapir、$^{11}$C-Pittsburgh compound-B、$^{18}$F-fluoro-2-deoxy-D-glucose和$^{15}$O-CO$_{2}$）临床脑PET数据对方法进行评估。与其他无监督方法和预训练模型相比，采用自监督预训练模型的去噪方法在保持空间细节和量化精度的同时，实现了鲁棒且最先进的去噪性能。这些结果表明，该方法对罕见疾病和示踪剂尤其有效，并有助于在不影响患者的情况下缩短扫描时间或降低放射性示踪剂剂量。