CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising

Qi Gao; Hongming Shan

doi:10.1117/12.2634939

14 October 2022 CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising

Qi Gao, Hongming Shan

Proceedings Volume 12242, Developments in X-Ray Tomography XIV; 122420I (2022) https://doi.org/10.1117/12.2634939
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Convolutional neural networks (CNNs) have been widely used for low-dose CT (LDCT) image denoising. To alleviate the over-smoothing effect caused by conventional mean squared error, researchers often resort to adversarial training to achieve faithful structural and texture recovery. On one hand, such adversarial training is typically difficult to train and may lead to a potential CT value shift. On the other hand, these CNNs-based denoising models usually generalize poorly to new unseen dose levels. Recently, diffusion models have exhibited higher image quality and stable trainability compared to other generative models. Therefore, we present a Contextual Conditional Diffusion model (CoCoDiff) for low-dose CT denoising, which aims to address the issues of existing denoising models. More specifically, during the training stage, we train a noise estimation network to gradually convert a residual image to a Gaussian distribution based on a Markov chain with a low-dose image as the condition. During the inference stage, the Markov chain is reversed to generate the residual image from random Gaussian noise. In addition, the contextual information of adjacent slices is also utilized for noise estimation to suppress potential structural distortion. Experimental results on Mayo LDCT datasets show that the proposed model can restore faithful structural details and generalize well to other unseen noise levels.

Conference Presentation

Citation Download Citation

Qi Gao and Hongming Shan "CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising", Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122420I (14 October 2022); https://doi.org/10.1117/12.2634939

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