Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET
  Image Reconstruction
Jiaqi Cui Yan Wang Lu Wen Pinxian Zeng Xi Wu Jiliu Zhou Dinggang Shen
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.