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重要任务。近年来，许多基于深度学习的方法已在该领域被证明有效。然而，这些方法大多遵循监督架构，需要全剂量与四分之一剂量的配对CT图像，且其解决方案高度依赖于特定测量条件。本文提出一种名为DDLM（Denoising Diffusion LDCT Model）的扩散去噪LDCT模型，通过条件采样生成无噪声CT图像。DDLM采用预训练模型，无需任何训练或调优过程，因而属于无监督方法。在LDCT图像上的实验表明，DDLM在缩短推理时间的同时取得了可比较的性能，超越了其他最先进的方法，兼具准确性与高效性。实现代码将尽快公开。