CoreDiff：用于低剂量CT去噪与泛化的上下文误差调制广义扩散模型
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
AI总结：
本文提出CoreDiff，一种用于低剂量CT去噪的上下文误差调制广义扩散模型，通过保均值退化算子减少采样步骤，并设计CLEAR-Net缓解误差累积，结合单样本学习框架实现快速泛化，在去噪和泛化性能上优于现有方法。
摘要：
低剂量计算机断层扫描（CT）图像由于光子饥饿和电子噪声而存在噪声和伪影。最近，一些工作尝试使用扩散模型来解决先前基于深度学习的去噪模型遇到的过度平滑和训练不稳定问题。然而，由于涉及大量采样步骤，扩散模型存在推理时间长的问题。最近，冷扩散模型推广了经典扩散模型并具有更大的灵活性。受冷扩散启发，本文提出了一种用于低剂量CT（LDCT）去噪的新型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，并采用一种新颖的保均值退化算子来模拟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，显著减少了采样步骤。其次，为了缓解采样过程中由不完美恢复算子导致的误差累积问题，我们提出了新型上下文误差调制恢复网络（CLEAR-Net），该网络能利用上下文信息约束采样过程防止结构畸变，并调制时间步嵌入特征以更好地与下一时间步的输入对齐。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仅使用一张与NDCT（未）配对的LDCT图像即可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优于竞争方法，且具有临床可接受的推理时间。源代码可在 https://github.com/qgao21/CoreDiff 获取。
英文摘要：
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.
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