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Edge Protection and Global Attention Mechanism Densely Connected Convolutional Network for LDCT Denoising

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Abstract

Low-dose computed tomography (LDCT) imaging can significantly reduce the radiation dose to a patient. However, a low radiation dose will cause considerable noise and artifacts in the image, seriously impacting the clinical diagnosis. To better solve the problems, we propose an edge protection and global attention mechanism densely connected convolutional network (EP–GAMNet) for LDCT denoising. First, edge information was extracted using the improved eight-directional Prewitt operator and then, passed to each convolutional block through skip connections. Subsequently, a multiscale feature extractor and global attention mechanism were used for feature extraction. The final predicted images were then obtained by the noise reduction module. Further, a compound loss function based on mean squared error and perceptual loss was used to enhance the texture detail and improve the visual quality of the image. Extensive experiments on Mayo and piglet datasets showed the effectiveness of the proposed method in reducing noise/artifacts while preserving edges. The peak-signal-to-noise ratio value of CT images of the AAPM dataset processed by the new model is 33.5712, and the structural similarity (SSIM) value is 0.9244. Moreover, it performed better than some classical methods in terms of objective indicators and subjective effects. The proposed model is robust and can effectively retain edge information and extract effective features from LDCT images. Moreover, it is effective in improving LDCT image quality.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the Open Fund Project of the Key Laboratory of Computer Network and Information Integration of the Ministry of Education (K93-9-2022-02), the Science and Technology Innovation Project of Colleges and Universities of Shanxi Province (2020L0282), the Key R&D plan of Shanxi Province (202102020101009), and the Natural Science Foundation of Shanxi Province (202103021224204).

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Authors and Affiliations

  1. State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China

    Jiaqi Kang, Yi Liu, Niu Guo, Quan Zhang, Zhiyuan Li & Zhiguo Gui

  2. School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China

    Jiaqi Kang, Yi Liu, Niu Guo, Quan Zhang, Zhiyuan Li & Zhiguo Gui

  3. Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 211189, Jiangsu, China

    Huazhong Shu

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  1. Jiaqi Kang
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Kang, J., Liu, Y., Shu, H. et al. Edge Protection and Global Attention Mechanism Densely Connected Convolutional Network for LDCT Denoising. Circuits Syst Signal Process 43, 941–964 (2024). https://doi.org/10.1007/s00034-023-02488-y

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