CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization.Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise.  Inspired by 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.  Third, to rapidly generalize the trained model to a new, unseen dose level with as few resources as possible, we devised a one-shot learning framework to make CoreDiff generalize faster and better using only one single LDCT image (un)paired with normal-dose CT (NDCT).  Extensive experimental results on four datasets demonstrate that our CoreDiff outperforms competing methods in denoising and generalization performance, with clinically acceptable inference time.
CoreDiff
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization.Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise.  Inspired by cold diffusion, this paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. 
Contextual Error-Modulated Generalized Diffusion Model
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization.Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. 
low-dose CT Denoising and Generalization