With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using deep learning. Although low-dose computed tomography reduces radiation exposure to patients, it also introduces more noise, which may interfere with visual interpretation by physicians and affect diagnostic results. To address this problem, inspired by Cycle-GAN for unsupervised learning, this paper proposes an end-to-end unsupervised low-dose computed tomography denoising framework. The proposed framework combines a U-Net structure for multi-scale feature extraction, an attention mechanism for feature fusion, and a residual network for feature transformation. It also introduces perceptual loss to improve the network for the characteristics of medical images. In addition, we construct a real low-dose computed tomography dataset and design a large number of comparative experiments to validate the proposed method, using both image-based evaluation metrics and medical evaluation criteria. Compared with classical methods, the main advantage of this paper is that it addresses the limitation that real clinical data cannot be directly used for supervised learning, while still achieving excellent performance. The experimental results are also professionally evaluated by imaging physicians and meet clinical needs.

翻译：随着深度学习的发展，医学图像处理已被广泛应用于辅助临床研究。本文聚焦于利用深度学习进行低剂量计算机断层扫描（CT）的去噪问题。尽管低剂量CT可减少患者所受辐射，但同时也引入了更多噪声，这可能干扰医生的视觉判读并影响诊断结果。为解决此问题，受无监督学习中的Cycle-GAN启发，本文提出了一种端到端的无监督低剂量CT去噪框架。该框架结合了用于多尺度特征提取的U-Net结构、用于特征融合的注意力机制以及用于特征变换的残差网络，并引入感知损失以针对医学图像特性优化网络。此外，我们构建了真实低剂量CT数据集，并设计了大量对比实验，采用基于图像的评估指标与医学评价准则来验证所提方法。与经典方法相比，本文的主要优势在于解决了真实临床数据无法直接用于监督学习的局限性，同时取得了优异性能。实验结果已由影像科医师进行专业评估，满足临床需求。