  Low-dose computed tomography (CT) images suffer from noise and artifacts due
to photon starvation and electronic noise. Recently, some works have attempted
to use diffusion models to address the over-smoothness and training instability
encountered by previous deep-learning-based denoising models. However,
diffusion models suffer from long inference times due to the large number of
sampling steps involved. Very recently, cold diffusion model generalizes
classical diffusion models and has greater flexibility. Inspired by the cold
diffusion, this paper presents a novel COntextual eRror-modulated gEneralized
Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First,
CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs
a novel mean-preserving degradation operator to mimic the physical process of
CT degradation, significantly reducing sampling steps thanks to the informative
LDCT images as the starting point of the sampling process. Second, to alleviate
the error accumulation problem caused by the imperfect restoration operator in
the sampling process, we propose a novel ContextuaL Error-modulAted Restoration
Network (CLEAR-Net), which can leverage contextual information to constrain the
sampling process from structural distortion and modulate time step embedding
features for better alignment with the input at the next time step. Third, to
rapidly generalize to a new, unseen dose level with as few resources as
possible, we devise a one-shot learning framework to make CoreDiff generalize
faster and better using only a single LDCT image (un)paired with NDCT.
Extensive experimental results on two datasets demonstrate that our CoreDiff
outperforms competing methods in denoising and generalization performance, with
a clinically acceptable inference time. Source code is made available at
https://github.com/qgao21/CoreDiff.
