Low-dose computed tomography (CT) image denoising is crucial in medical image computing. Recent years have been remarkable improvement in deep learning-based methods for this task. However, training deep denoising neural networks requires low-dose and normal-dose CT image pairs, which are difficult to obtain in the clinic settings. To address this challenge, we propose a novel fully unsupervised method for low-dose CT image denoising, which is based on denoising diffusion probabilistic model -- a powerful generative model. First, we train an unconditional denoising diffusion probabilistic model capable of generating high-quality normal-dose CT images from random noise. Subsequently, the probabilistic priors of the pre-trained diffusion model are incorporated into a Maximum A Posteriori (MAP) estimation framework for iteratively solving the image denoising problem. Our method ensures the diffusion model produces high-quality normal-dose CT images while keeping the image content consistent with the input low-dose CT images. We evaluate our method on a widely used low-dose CT image denoising benchmark, and it outperforms several supervised low-dose CT image denoising methods in terms of both quantitative and visual performance.

翻译：低剂量计算机断层扫描（CT）图像去噪在医学图像计算中至关重要。近年来，基于深度学习的相关方法取得了显著进展。然而，训练深度去噪神经网络需要低剂量与正常剂量CT图像配对数据，这在临床环境中难以获取。为解决这一挑战，我们提出了一种全新的无监督低剂量CT图像去噪方法，其基于去噪扩散概率模型（一种强大的生成模型）。首先，我们训练一个无条件的去噪扩散概率模型，使其能够从随机噪声生成高质量的正常剂量CT图像。随后，将预训练扩散模型的概率先验纳入最大后验（MAP）估计框架中，以迭代求解图像去噪问题。我们的方法确保扩散模型生成高质量正常剂量CT图像的同时，保持图像内容与输入低剂量CT图像的一致性。我们在广泛使用的低剂量CT图像去噪基准上评估了该方法，其在定量指标与视觉质量上均优于多种有监督低剂量CT图像去噪方法。