  As PET imaging is accompanied by radiation exposure and potentially increased
cancer risk, reducing radiation dose in PET scans without compromising the
image quality is an important topic. Deep learning (DL) techniques have been
investigated for low-dose PET imaging. However, existing models have often
resulted in compromised image quality when achieving low-dose PET and have
limited generalizability to different image noise-levels, acquisition
protocols, patient populations, and hospitals. 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 medical imaging tasks.
However, for low-dose PET imaging, existing diffusion models failed to generate
consistent 3D reconstructions, unable to generalize across varying
noise-levels, often produced visually-appealing but distorted image details,
and produced images with biased tracer uptake. Here, we develop DDPET-3D, a
dose-aware diffusion model for 3D low-dose PET imaging to address these
challenges. Collected from 4 medical centers globally with different scanners
and clinical protocols, we extensively evaluated the proposed model using a
total of 9,783 18F-FDG studies (1,596 patients) with low-dose/low-count levels
ranging from 1% to 50%. With a cross-center, cross-scanner validation, the
proposed DDPET-3D demonstrated its potential to generalize to different
low-dose levels, different scanners, and different clinical protocols. As
confirmed with reader studies performed by nuclear medicine physicians, the
proposed method produced superior denoised results that are comparable to or
even better than the 100% full-count images as well as previous DL baselines.
The presented results show the potential of achieving low-dose PET while
maintaining image quality. Lastly, a group of real low-dose scans was also
included for evaluation.
