低剂量CT技术旨在通过估计高分辨率正常剂量CT图像来降低患者的辐射暴露，以减少辐射诱发癌症的风险。近年来，许多深度学习方法已被提出，通过构建低剂量CT图像与其高剂量对应物之间的映射函数来解决这个问题。然而，这些方法中大多数忽视了不同辐射剂量对最终CT图像的影响，导致CT图像中可观察到的噪声强度差异较大。此外，低剂量CT图像的噪声强度在不同医疗设备制造商之间存在显著差异。在本文中，我们提出了一种多级噪声感知网络（MLNAN），该网络采用受约束的循环Wasserstein生成对抗网络来恢复具有不确定噪声水平的低剂量CT图像。特别地，噪声水平分类被预测并在生成器网络中作为先验模式进行重用。此外，鉴别器网络引入噪声水平的确定。在两种剂量减少策略下，我们对提出的方法在两个数据集上进行实验，包括模拟临床AAPM挑战数据集和来自United Imaging Healthcare（UIH）的商业CT数据集。实验结果显示，与其他几种基于深度学习的方法相比，我们提出的方法在噪声抑制和结构细节保持方面具有有效性。消融研究验证了各个组件在改善性能方面的有效性。未来的研究需要进一步探索实际临床应用和其他医学模式的可能性。版权所有©2023 Elsevier B.V. 保留所有权利。

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.Copyright © 2023 Elsevier B.V. All rights reserved.