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

Wenjun Xia; Qing Lyu; Ge Wang

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arXiv · 2209.15136

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

Wenjun Xia·Qing Lyu·Ge Wang

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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.

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BibTeX RIS

Wenjun Xia, Qing Lyu, Ge Wang. 2022-09-29. Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup. https://arxiv.org/abs/2209.15136

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