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.

翻译：低剂量计算机断层扫描（CT）图像因光子匮乏和电子噪声而遭受噪声和伪影的困扰。近期，部分研究尝试利用扩散模型来克服以往基于深度学习的去噪模型所面临的过度平滑与训练不稳定性问题。然而，扩散模型因涉及大量采样步数而存在推理时间过长的缺陷。近期，冷扩散模型将经典扩散模型进一步泛化，并展现出更强的灵活性。受冷扩散模型的启发，本文提出一种新颖的上下文误差调制广义扩散模型用于低剂量CT（LDCT）去噪，命名为CoreDiff。首先，CoreDiff利用LDCT图像替代随机高斯噪声，并引入一种新颖的均值保持退化算子来模拟CT退化的物理过程，由于以信息丰富的LDCT图像作为采样过程的起点，显著减少了采样步数。其次，为缓解采样过程中因非完美恢复算子导致的误差累积问题，我们提出一种新颖的上下文误差调制恢复网络（CLEAR-Net），该网络能够利用上下文信息约束采样过程以避免结构失真，并调制时间步嵌入特征以使其与下一时间步的输入更好对齐。第三，为在尽可能少资源条件下快速泛化至新的未知剂量水平，我们设计了一种单样本学习框架，使CoreDiff仅凭单一LDCT图像（与NDCT配对或未配对）即可实现更快且更优的泛化能力。在两个数据集上的大量实验结果表明，我们的CoreDiff在去噪与泛化性能上均优于对比方法，且推理时间满足临床可接受范围。源代码已发布于https://github.com/qgao21/CoreDiff。