Unsupervised learning-based dual-domain method for low-dose CT denoising

Yu，Jie1; Zhang，Huitao1,2; Zhang，Peng1; Zhu，Yining1,2

2023-09-08

10.1088/1361-6560/acefa2

Physics in medicine and biology   Impact Factor & Quartile

0031-9155

1361-6560

Objective. Low-dose CT (LDCT) is an important research topic in the field of CT imaging because of its ability to reduce radiation damage in clinical diagnosis. In recent years, deep learning techniques have been widely applied in LDCT imaging and a large number of denoising methods have been proposed. However, One major challenge of supervised deep learning-based methods is the exactly geometric pairing of datasets with different doses. Therefore, the aim of this study is to develop an unsupervised learning-based LDCT imaging method to address the aforementioned challenges.Approach. In this paper, we propose an unsupervised learning-based dual-domain method for LDCT denoising, which consists of two stages: the first stage is projection domain denoising, in which the unsupervised learning method Noise2Self is applied to denoise the projection data with statistically independent and zero-mean noise. The second stage is an iterative enhancement approach, which combines the prior information obtained from the generative model with an iterative reconstruction algorithm to enhance the details of the reconstructed image.Main results. Experimental results show that our proposed method outperforms the comparison method in terms of denoising effect. Particularly, in terms of SSIM, the denoised results obtained using our method achieve the highest SSIM.Significance. In conclusion, our unsupervised learning-based method can be a promising alternative to the traditional supervised methods for LDCT imaging, especially when the availability of the labeled datasets is limited.

dual-domain method low-dose CT unsupervised denoising

SCI

English

Others

National Natural Science Foundation of China["61971293","61871275"] ; Sino-German Center[M-0187]

Engineering ; Radiology, Nuclear Medicine & Medical Imaging

Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging

WOS:001061504100001

IOP Publishing Ltd

MOLECULAR BIOLOGY & GENETICS

2-s2.0-85170438764

Scopus

1.School of Mathematical Sciences,Capital Normal University,100048,China
2.Shenzhen National Applied Mathematics Center,Southern University of Science and Technology,Shenzhen,518055,China

Yu，Jie,Zhang，Huitao,Zhang，Peng,et al. Unsupervised learning-based dual-domain method for low-dose CT denoising[J]. Physics in medicine and biology,2023,68(18).

Yu，Jie,Zhang，Huitao,Zhang，Peng,&Zhu，Yining.(2023).Unsupervised learning-based dual-domain method for low-dose CT denoising.Physics in medicine and biology,68(18).

Yu，Jie,et al."Unsupervised learning-based dual-domain method for low-dose CT denoising".Physics in medicine and biology 68.18(2023).