Abstract
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.
Abstract (translated)
低剂量核磁共振(CT)图像去噪在医学图像计算中至关重要。近年来，基于深度学习的方法在该领域取得了显著的进展。然而，训练深度去噪神经网络需要低剂量和正常剂量的CT图像对，这在临床 settings 中很难获取。为了解决这一挑战，我们提出了一种全新的完全 unsupervised 的方法，用于低剂量CT图像去噪，其基于去噪扩散概率模型，这是一种强大的生成模型。我们首先训练一个无条件去噪扩散概率模型，可以从随机噪声中生成高质量的正常剂量CT图像。随后，我们训练了预先训练的扩散模型的概率前向量，并将其融入最大后效估计框架中，以迭代地解决图像去噪问题。我们的方法和输入的低剂量CT图像的图像内容保持一致，确保了扩散模型生成高质量的正常剂量CT图像，同时保持图像质量与输入的低剂量CT图像相似。我们使用了一个广泛应用的低剂量CT图像去噪基准进行评估，该方法在 quantitative 和 visual 性能上均优于 several supervised low-剂量CT图像去噪方法。
URL
https://arxiv.org/abs/2305.15887
