Diffusion Denoising for Low-Dose-CT Model

TL;DR

This paper introduces DDLM, an unsupervised diffusion model for low-dose CT reconstruction that generates noise-free images efficiently without requiring training, outperforming existing methods.

Contribution

The novel DDLM approach uses pretrained diffusion models for LDCT reconstruction in an unsupervised manner, eliminating the need for paired training data.

Findings

Comparable performance to state-of-the-art methods

Less inference time

No training or tuning required

Abstract

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.