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Low Dose CT Image Denoising using Deep Convolutional Neural Networks with Extended Receptive Fields
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  • Nguyen Thanh-Trung ,
  • Dinh-Hoan Trinh ,
  • Nguyen Linh-Trung ,
  • Marie Luong
Nguyen Thanh-Trung
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Dinh-Hoan Trinh
Advanced Institute of Engineering and Technology

Corresponding Author:[email protected]

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Nguyen Linh-Trung
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Marie Luong
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How to reduce dose radiation while preserving the image quality as when using standard dose is one of the most important topics in the Computed Tomography (CT) imaging domain due to 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, first, gives 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 and thus effectively denoises. Experimental results showed that the proposed method is light while its denoising effectiveness is competitive with well-known CNN-based models.