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

POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation