Diffusion Denoising for Low-Dose-CT Model
This addresses the problem of reducing radiation exposure in medical imaging for patients, though it is incremental as it builds on existing diffusion models.
The paper tackles low-dose CT reconstruction by introducing DDLM, an unsupervised diffusion-based method that generates noise-free images without training, achieving comparable performance to state-of-the-art methods with less inference time.
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.