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：低剂量计算地形学(LDCT)的重建是医学图像分析中的一项重要任务。近年来,许多基于深层次学习的方法都取得了成效,但是,这些方法大多遵循受监督的结构,需要将全剂量和四分之一剂量的CT图像配对,而溶液则在很大程度上取决于具体的测量。在这项工作中,我们引入了Donoising Distroising Difmission LDCT模型,称为DDLM,使用有条件的取样产生无噪音的CT图像。DDLM使用预先培训的模型,不需要培训或调控程序,因此我们的提案是不受监督的。对LDCT图像的实验显示DDLM的类似性能,使用了较少的推论时间,超过了其他最先进的方法,证明是准确和有效的。实施代码将很快公诸于众。