POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for
  Low-Count PET Attenuation Map Generation
Bo Zhou Jun Hou Tianqi Chen Yinchi Zhou Xiongchao Chen Huidong Xie Qiong Liu Xueqi Guo Yu-Jung Tsai Vladimir Y. Panin Takuya Toyonaga James S. Duncan Chi Liu
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.