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