Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup

2022/09/29 by Xia, Wenjun, Lyu, Qing, Wang, Ge · 1 citation
#FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2209.15136

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.

Cited by

• D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images

Related

• Diffusion Denoising for Low-Dose-CT Model
• Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model
• Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
• AST-n: A Fast Sampling Approach for Low-Dose CT Reconstruction using Diffusion Models
• Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT