To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images. However, these methods heavily rely on voxel-based representations, which fall short of adequately accounting for the precise structure and fine-grained context, leading to compromised reconstruction. In this paper, we propose a 3D point-based context clusters GAN, namely PCC-GAN, to reconstruct high-quality SPET images from LPET. Specifically, inspired by the geometric representation power of points, we resort to a point-based representation to enhance the explicit expression of the image structure, thus facilitating the reconstruction with finer details. Moreover, a context clustering strategy is applied to explore the contextual relationships among points, which mitigates the ambiguities of small structures in the reconstructed images. Experiments on both clinical and phantom datasets demonstrate that our PCC-GAN outperforms the state-of-the-art reconstruction methods qualitatively and quantitatively. Code is available at https://github.com/gluucose/PCCGAN.

翻译：摘要：为在减少辐射暴露的同时获得高质量正电子发射断层扫描（PET）图像，研究者提出了多种方法，通过低剂量PET（LPET）图像重建标准剂量PET（SPET）图像。然而，现有方法过度依赖基于体素的表示，难以充分捕捉精确的结构与细粒度上下文信息，导致重建质量受损。本文提出一种基于3D点云的上下文聚类生成对抗网络（PCC-GAN），可从LPET图像重建高质量SPET图像。具体而言，受点云几何表征能力的启发，我们采用点云表示增强图像结构的显式表达，从而促进更精细细节的重建。此外，通过上下文聚类策略探索点之间的上下文关系，有效缓解重建图像中小结构区域的模糊性。在临床与体模数据集上的实验表明，PCC-GAN在定性与定量上均优于现有最先进的重建方法。代码开源地址：https://github.com/gluucose/PCCGAN。