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.

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包含噪声和伪影。近期，一些研究尝试使用扩散模型来克服以往基于深度学习的去噪模型所遇到的过平滑和训练不稳定问题。然而，扩散模型因涉及大量采样步骤而导致推理时间较长。最近，冷扩散模型将经典扩散模型进行了泛化，并展现出更强的灵活性。受冷扩散启发，本文提出了一种新颖的面向低剂量CT（LDCT）去噪的上下文误差调制广义扩散模型，称为CoreDiff。首先，CoreDiff利用LDCT图像替代随机高斯噪声，并采用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，这显著减少了采样步骤。其次，为了缓解采样过程中因不完美恢复算子导致的误差累积问题，我们提出了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，避免结构失真，并调制时间步嵌入特征，使其与下一时间步的输入更好对齐。第三，为了以尽可能少的资源快速泛化到新的、未见过的剂量水平，我们设计了一个单样本学习框架，使CoreDiff能够仅使用单张（非）配对NDCT的LDCT图像，实现更快、更好的泛化。在两个数据集上的大量实验结果表明，我们的CoreDiff在去噪和泛化性能上均优于对比方法，且推理时间在临床可接受范围内。