Low-dose Computed Tomography (LDCT) reconstruction is an important task in medical image analysis. Recent years have seen many deep learning based methods, proved to be effective in this area. However, these methods mostly follow a supervised architecture, which needs paired CT image of full dose and quarter dose, and the solution is highly dependent on specific measurements. In this work, we introduce Denoising Diffusion LDCT Model, dubbed as DDLM, generating noise-free CT image using conditioned sampling. DDLM uses pretrained model, and need no training nor tuning process, thus our proposal is in unsupervised manner. Experiments on LDCT images have shown comparable performance of DDLM using less inference time, surpassing other state-of-the-art methods, proving both accurate and efficient. Implementation code will be set to public soon.

翻译：低剂量计算机断层扫描（LDCT）重建是医学图像分析中的一项重要任务。近年来，许多基于深度学习的方法被证明在该领域具有有效性。然而，这些方法大多遵循有监督架构，需要全剂量与四分之一剂量的配对CT图像，且解决方案高度依赖于特定的测量条件。在本工作中，我们提出了一种名为DDLM（Denoising Diffusion LDCT Model）的去噪扩散LDCT模型，通过条件采样生成无噪声的CT图像。DDLM使用预训练模型，无需任何训练或微调过程，因此属于无监督方法。在LDCT图像上的实验表明，DDLM在缩短推理时间的同时实现了与现有最优方法相当的性能，验证了其准确性与高效性。实现代码将很快公开。