Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup

TL;DR

This paper introduces a conditional denoising diffusion probabilistic model for low-dose CT denoising, achieving 20x faster sampling with maintained image quality, enhancing diagnostic performance while reducing radiation risks.

Contribution

The paper presents a novel application of DDPM with a fast ODE solver for efficient low-dose CT denoising, significantly improving sampling speed.

Findings

Achieved 20x speedup in sampling efficiency.

Maintained high image quality in denoised CT images.

Demonstrated potential for clinical application in radiology.

Abstract

Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equation (ODE) solver for a much-improved sampling efficiency. The experiments show that the accelerated DDPM can achieve 20x speedup without compromising image quality.