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Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields

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Abstract

How to reduce radiation dose while preserving the image quality as when using standard dose is an important topic in the computed tomography (CT) imaging domain because the quality of low-dose CT (LDCT) images is often strongly affected by noise and artifacts. Recently, there has been considerable interest in using deep learning as a post-processing step to improve the quality of reconstructed LDCT images. This paper provides, first, an overview of learning-based LDCT image denoising methods from patch-based early learning methods to state-of-the-art CNN-based ones and, then, a novel CNN-based method is presented. In the proposed method, preprocessing and post-processing techniques are integrated into a dilated convolutional neural network to extend receptive fields. Hence, large distance pixels in input images will participate in enriching feature maps of the learned model, leading to effective denoising. Experimental results showed that the proposed method is light, while its denoising effectiveness is competitive with well-known CNN-based models.

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Authors and Affiliations

  1. Advanced Institute of Engineering and Technology, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

    Nguyen Thanh Trung & Nguyen Linh Trung

  2. Viettel Cyberspace Center, Viettel Group, Hanoi, Vietnam

    Dinh-Hoan Trinh

  3. University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Vietnam

    Nguyen Thanh Trung

  4. L2TI Laboratory, Galillee Institute, University Paris 13, Villetaneuse, France

    Marie Luong

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  1. Nguyen Thanh Trung
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  2. Dinh-Hoan Trinh
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  3. Nguyen Linh Trung
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  4. Marie Luong
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Correspondence to Dinh-Hoan Trinh or Nguyen Linh Trung.

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Trung, N.T., Trinh, DH., Trung, N.L. et al. Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields. SIViP 16, 1963–1971 (2022). https://doi.org/10.1007/s11760-022-02157-8

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  • DOI: https://doi.org/10.1007/s11760-022-02157-8

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