Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps (u-map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose POUR-Net - an innovative population-prior-aided over-under-representation network that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an over-under-representation network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived u-map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of $\mu$-map generation, resulting in the production of high-quality $\mu$-maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods.

翻译：低剂量PET为减少PET成像中的辐射暴露提供了一种有价值的手段。然而，目前普遍采用额外CT扫描生成PET衰减校正所需的衰减图（μ-map）的做法会显著增加辐射剂量。为解决这一问题并进一步降低低剂量PET检查中的辐射暴露，我们提出了POUR-Net——一种创新的群体先验辅助的过欠表示网络，旨在从低剂量PET生成高质量的衰减图。首先，POUR-Net集成了一个过欠表示网络（OUR-Net），以促进高效的特征提取，涵盖低分辨率抽象特征和精细细节特征，从而辅助全分辨率水平上的深度生成。其次，为补充OUR-Net，一个利用全面CT衍生μ-map数据集的群体先验生成机（PPGM）提供了额外的先验信息以辅助OUR-Net的生成。通过级联框架整合OUR-Net和PPGM，实现了对μ-map生成的迭代优化，从而产生高质量的μ-map。实验结果验证了POUR-Net的有效性，表明其作为一种精确的无CT低计数PET衰减校正方案具有前景，并超越了先前基线方法的性能。