  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.
