Positron emission tomography (PET) is a widely used, highly sensitive
molecular imaging in clinical diagnosis. There is interest in reducing the
radiation exposure from PET but also maintaining adequate image quality. Recent
methods using convolutional neural networks (CNNs) to generate synthesized
high-quality PET images from low-dose counterparts have been reported to be
state-of-the-art for low-to-high image recovery methods. However, these methods
are prone to exhibiting discrepancies in texture and structure between
synthesized and real images. Furthermore, the distribution shift between
low-dose PET and standard PET has not been fully investigated. To address these
issues, we developed a self-supervised adaptive residual estimation generative
adversarial network (SS-AEGAN). We introduce (1) An adaptive residual
estimation mapping mechanism, AE-Net, designed to dynamically rectify the
preliminary synthesized PET images by taking the residual map between the
low-dose PET and synthesized output as the input, and (2) A self-supervised
pre-training strategy to enhance the feature representation of the coarse
generator. Our experiments with a public benchmark dataset of total-body PET
images show that SS-AEGAN consistently outperformed the state-of-the-art
synthesis methods with various dose reduction factors.

通过自我监督的自适应残差估计生成对抗网络（SS-AEGAN），我们引入了自适应残差估计映射机制 AE-Net，并采用自我监督的预训练策略来提高粗糙生成器的特征表示，实验证明 SS-AEGAN 在各种剂量降低因素下，始终优于最先进的合成方法。