Low‐dose CT denoising using a diffusion prior via score distillation sampling
Hojin Jung, Jooho Lee, Jongduk BaekAbstract
Background
Low‐dose computed tomography (LDCT) reduces radiation exposure but amplifies noise, which can degrade diagnostic reliability. Recent studies have applied diffusion‐based models to LDCT denoising and shown promising performance. However, many of these approaches require paired LDCT and normal‐dose CT (NDCT) data or dose‐specific retraining, and their reconstruction procedures are often strictly coupled to a predefined diffusion trajectory.
Purpose
This study proposes an optimization‐based LDCT denoising framework that uses a pre‐trained NDCT diffusion model as an image prior and can be applied across various dose levels without dose‐specific retraining.
Methods
We formulate LDCT denoising as an optimization problem guided by a pre‐trained score‐based generative model (SGM) trained only on NDCT images. The Poisson data fidelity term enforces consistency with the measured projection data, whereas the score distillation sampling (SDS) term acts as a regularizer that guides the reconstruction toward the NDCT distribution learned by the pre‐trained SGM. The denoised image is obtained by iteratively optimizing a projection‐domain residual variable using the combined Poisson data fidelity gradient and SDS‐based regularization gradient.
Results
The proposed method effectively suppressed noise while preserving fine anatomical structures across multiple dose levels using a single pre‐trained diffusion model. Quantitative evaluations with respect to full‐dose reference images and visual comparisons demonstrated competitive performance compared with supervised and self‐supervised LDCT denoising baselines.
Conclusions
The results show that diffusion‐based image priors can be leveraged across various low‐dose levels within the proposed optimization‐based denoising framework. Our work suggests that SDS‐guided diffusion priors are a promising direction for LDCT denoising in data‐limited settings.