A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network

Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic infor...

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Vydáno v:IEEE journal of biomedical and health informatics Ročník 26; číslo 7; s. 3251 - 3260
Hlavní autoři: Han, Zefang, Shangguan, Hong, Zhang, Xiong, Zhang, Pengcheng, Cui, Xueying, Ren, Huiying
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Bioinformatics
Coders
Complementation
Computed tomography
Encoder-decoder structure
Feature extraction
Generative adversarial network
Generative adversarial networks
hierarchical-split ResNet
image denoising
Image edge detection
Image quality
LDCT
Medical imaging
Modules
Noise reduction
Quality control
Redundancy
Self-similarity
Semantics
Structural hierarchy
Testing
X-ray imaging
ISSN:2168-2194, 2168-2208, 2168-2208
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Popis
Shrnutí:Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity; Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator; Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://github.com/hanzefang/DESDGAN .
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2022.3155788