Prof. Guoxi Xie Profile

Prof. Guoxi Xie

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Proceedings Article | 7 April 2023 Poster + Paper

Unpaired learning with a data-dependent noise-generative model for low-dose CT sinogram restoration

Yang Liu, Shumao Pang, Dong Zeng, Guoxi Xie, Jianhua Ma, Ji He

Proceedings Volume 12463, 124631Y (2023) https://doi.org/10.1117/12.2649781

KEYWORDS: X-ray computed tomography, Data modeling, X-rays, Network architectures, Computed tomography, Adversarial training

Read Abstract +

Low-dose computed tomography (CT) is of great potential advantage for disease diagnosis. Usually, paired training datasets are difficult to obtain in clinical routine, which catalyzes the development of unsupervised learning techniques to improve the low-dose CT imaging. Recently, most existing unsupervised learning approaches for low-dose CT imaging were developed in the image domain, and only a few approaches have been developed in the sinogram domain, which is a challenging task. In this paper, we propose a dedicated unpaired learning technique for low-dose CT sinogram restoration with a novel data-dependent noise-generative model. The general idea is to construct a paired pseudo normal-/low-dose sinogram dataset based on the existing unpaired normal-/low-dose sinogram dataset, after which a sinogram restoration network can be obtained by training on the paired pseudo normal-/low-dose sinogram dataset. However, the difficulty of the presented idea lies in the construction of the pseudo low-dose sinogram generative network, due to the complexity of the texture feature and noise property in the sinogram domain. To address this issue, we construct an appropriative generative network architecture based on a reasonable noise-generative model in the sinogram domain, which can be used to produce pseudo low-dose sinogram data within an adversarial learning framework. To validate the proposed technique, a clinical dataset was adopted. Experimental results demonstrate that the proposed method can produce promising pseudo low-dose sinogram data, which is sufficient to train an effective sinogram restoration network. Both quantitative and qualitative measurements show that the proposed method can obtained promising low-dose CT imaging performance.

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