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As PET imaging is accompanied by substantial radiation exposure and cancerrisk, reducing radiation dose in PET scans is an important topic. Recently,diffusion models have emerged as the new state-of-the-art generative model togenerate high-quality samples and have demonstrated strong potential forvarious tasks in medical imaging. However, it is difficult to extend diffusionmodels for 3D image reconstructions due to the memory burden. Directly stacking2D slices together to create 3D image volumes would results in severeinconsistencies between slices. Previous works tried to either apply a penaltyterm along the z-axis to remove inconsistencies or reconstruct the 3D imagevolumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless,these previous methods failed to produce satisfactory results in challengingcases for PET image denoising. In addition to administered dose, the noiselevels in PET images are affected by several other factors in clinicalsettings, e.g. scan time, medical history, patient size, and weight, etc.Therefore, a method to simultaneously denoise PET images with differentnoise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3Dlow-dose PET imaging (DDPET-3D) to address these challenges. We extensivelyevaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of600 testing studies), and demonstrated superior performance over previousdiffusion models for 3D imaging problems as well as previous noise-awaremedical image denoising models. The code is available at:https://github.com/xxx/xxx.

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging