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.

翻译：正电子发射断层扫描（PET）是一种广泛应用于临床诊断的高灵敏分子成像技术。如何在降低PET辐射暴露的同时保持足够的图像质量，是当前研究的热点。近期研究表明，采用卷积神经网络（CNN）从低剂量PET生成高质量合成图像的方法，在低剂量到高剂量图像恢复领域达到了最先进水平。然而，这些方法合成的图像在纹理和结构上与真实图像存在偏差。此外，低剂量PET与标准PET之间的分布偏移尚未得到充分研究。为解决上述问题，我们提出了一种自监督自适应残差估计生成对抗网络（SS-AEGAN）。该网络引入：（1）自适应残差估计映射机制AE-Net，通过将低剂量PET与合成输出之间的残差图作为输入，动态修正初步合成的PET图像；（2）自监督预训练策略，增强粗生成器的特征表达能力。我们在公开的全身体PET基准数据集上的实验表明，SS-AEGAN在不同剂量降低因子下均持续优于现有最先进的合成方法。