Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low-dose and normal-dose CT images for training, which are challenging to obtain in clinical settings. Existing unsupervised deep learning-based methods often require training with a large number of low-dose CT images or rely on specially designed data acquisition processes to obtain training data. To address these limitations, we propose a novel unsupervised method that only utilizes normal-dose CT images during training, enabling zero-shot denoising of low-dose CT images. Our method leverages the diffusion model, a powerful generative model. We begin by training a cascaded unconditional diffusion model capable of generating high-quality normal-dose CT images from low-resolution to high-resolution. The cascaded architecture makes the training of high-resolution diffusion models more feasible. Subsequently, we introduce low-dose CT images into the reverse process of the diffusion model as likelihood, combined with the priors provided by the diffusion model and iteratively solve multiple maximum a posteriori (MAP) problems to achieve denoising. Additionally, we propose methods to adaptively adjust the coefficients that balance the likelihood and prior in MAP estimations, allowing for adaptation to different noise levels in low-dose CT images. We test our method on low-dose CT datasets of different regions with varying dose levels. The results demonstrate that our method outperforms the state-of-the-art unsupervised method and surpasses several supervised deep learning-based methods. Codes are available in https://github.com/DeepXuan/Dn-Dp.

翻译：低剂量计算机断层扫描（CT）图像去噪是医学图像计算中的关键任务。近年来，基于监督深度学习的方法在该领域取得了显著进展。然而，这些方法通常需要成对的低剂量和正常剂量CT图像进行训练，这在临床环境中难以获取。现有的基于无监督深度学习的方法通常需要大量低剂量CT图像进行训练，或依赖专门设计的数据采集过程来获取训练数据。为克服这些限制，我们提出一种新颖的无监督方法，该方法在训练期间仅使用正常剂量CT图像，从而实现低剂量CT图像的零样本去噪。我们的方法利用了扩散模型这一强大的生成模型。首先，我们训练一个级联的无条件扩散模型，该模型能够从低分辨率到高分辨率生成高质量的正常剂量CT图像。级联架构使得高分辨率扩散模型的训练更加可行。随后，我们将低剂量CT图像作为似然引入扩散模型的反向过程中，结合扩散模型提供的先验，通过迭代求解多个最大后验（MAP）问题来实现去噪。此外，我们提出自适应调整MAP估计中平衡似然与先验的系数的方法，从而适应不同噪声水平的低剂量CT图像。我们在不同区域、不同剂量水平的低剂量CT数据集上测试了该方法。结果表明，我们的方法优于最先进的无监督方法，并超越了多种基于监督深度学习的方法。代码可在 https://github.com/DeepXuan/Dn-Dp 获取。