A Low-Dose CT Image Denoising Method Based on Generative Adversarial Network and Noise Level Estimation

Xiong ZHANG; Linlin YANG; Hong SHANGGUAN; Zefang HAN; Xinglong HAN; Anhong WANG; Xueying CUI

doi:10.11999/JEIT200591

Volume 43 Issue 8

Aug. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(8): 2404-2413

Xiong ZHANG, Linlin YANG, Hong SHANGGUAN, Zefang HAN, Xinglong HAN, Anhong WANG, Xueying CUI. A Low-Dose CT Image Denoising Method Based on Generative Adversarial Network and Noise Level Estimation[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2404-2413. doi: 10.11999/JEIT200591

Citation:

Xiong ZHANG, Linlin YANG, Hong SHANGGUAN, Zefang HAN, Xinglong HAN, Anhong WANG, Xueying CUI. A Low-Dose CT Image Denoising Method Based on Generative Adversarial Network and Noise Level Estimation[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2404-2413. doi: 10.11999/JEIT200591

Citation:

Xiong ZHANG, Linlin YANG, Hong SHANGGUAN, Zefang HAN, Xinglong HAN, Anhong WANG, Xueying CUI. A Low-Dose CT Image Denoising Method Based on Generative Adversarial Network and Noise Level Estimation[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2404-2413. doi: 10.11999/JEIT200591

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A Low-Dose CT Image Denoising Method Based on Generative Adversarial Network and Noise Level Estimation

doi: 10.11999/JEIT200591

School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Funds: The Natural Science for Youth Foundation (62001321), The Scientific and Technological Innovation Programs of Higher Educations Institutions in Shanxi (2019L0642), The Natural Science Foundation of Shanxi Province (201901D111261)

Received Date: 2020-07-17
Rev Recd Date: 2021-02-03

Available Online: 2021-03-01

Publish Date: 2021-08-10

Abstract

Abstract

Generative Adversarial Network (GAN) for Low-Dose CT (LDCT) image noise reduction has certain performance advantages, and has become a new research hot field of CT image noise reduction in recent years. When the intensity of noise and artifact distribution changes in LDCT images of different doses, the noise reduction performance of GAN network is unstable, and the generalization ability of the network is low. In order to overcome these shortcomings, this paper first designs a noise level estimation subnet with a encoder-decoder structure to generate the noise maps corresponding to LDCT images with different doses, which is subtracted from the original input image to initially suppress the noise; Secondly, the backbone of the noise reduction network is designed as a multi-coded U-Net structure that is optimized through game confrontation to suppress further CT image noise; Finally, a variety of loss functions are designed to constrain the parameter optimization of each function modules, thus to guarantee further the performance of the LDCT image noise reduction network. Experimental results show that compared with current popular algorithms, the noise reduction network proposed in this paper can achieve a better noise reduction on the basis of retaining the original important information of LDCT images.
- Image denoising,
- Generative Adversarial Network (GAN),
- Low-Dose CT (LDCT),
- U-Net,
- Noise level

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References(22)

References

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