This paper presents a robust multi-domain network designed to restore low-quality amyloid PET images acquired in a short period of time. The proposed method is trained on pairs of PET images from short (2 minutes) and standard (20 minutes) scanning times, sourced from multiple domains. Learning relevant image features between these domains with a single network is challenging. Our key contribution is the introduction of a mapping label, which enables effective learning of specific representations between different domains. The network, trained with various mapping labels, can efficiently correct amyloid PET datasets in multiple training domains and unseen domains, such as those obtained with new radiotracers, acquisition protocols, or PET scanners. Internal, temporal, and external validations demonstrate the effectiveness of the proposed method. Notably, for external validation datasets from unseen domains, the proposed method achieved comparable or superior results relative to methods trained with these datasets, in terms of quantitative metrics such as normalized root mean-square error and structure similarity index measure. Two nuclear medicine physicians evaluated the amyloid status as positive or negative for the external validation datasets, with accuracies of 0.970 and 0.930 for readers 1 and 2, respectively.

翻译：本文提出了一种鲁棒多域网络，旨在恢复短时采集的低质量淀粉样蛋白PET图像。该方法基于来自多个域、短扫描时间（2分钟）与标准扫描时间（20分钟）的PET图像对进行训练。使用单一网络学习这些域间的相关图像特征具有挑战性。我们的关键贡献在于引入映射标签，从而有效学习不同域间的特定表征。该网络通过多种映射标签训练，可高效校正多个训练域及未见域（如采用新型示踪剂、采集协议或PET扫描仪获取的数据）中的淀粉样蛋白PET数据集。内部验证、时间验证及外部验证均证明该方法的有效性。值得注意的是，针对来自未见域的外部验证数据集，该方法在归一化均方根误差和结构相似性指数等定量指标上，相较于基于这些数据集训练的方法取得了相当或更优的结果。两位核医学医师对外部验证数据集的淀粉样蛋白状态进行阳性/阴性判定，读者1和读者2的准确率分别达到0.970和0.930。