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• W2979556105 abstract "Low-dose CT imaging is a valid approach to reduce patients' exposure to X-ray radiation. However, reducing X-ray current increases noise and artifacts in the reconstructed CT images. Deep neural networks have been successfully employed to remove noise from low-dose CT images. This study proposes two novel techniques to boost the performance of a neural network with minimal change in the complexity. First, a non-trainable edge detection layer is proposed that extracts four edge maps from the input image. The layer improves quantitative metrics (PSNR and SSIM) and helps to predict a CT image with more precise boundaries. Next, a joint function of mean-square error and perceptual loss is employed to optimize the network. Using the perceptual loss helps to preserve structural detail; however, it adds check-board artifacts to the output. The proposed joint objective function takes advantage of the benefits offered by each loss. It improves the over-smoothing problem caused by mean-square error and the check-board artifacts caused by perceptual loss." @default.
• W2979556105 created "2019-10-18" @default.
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• W2979556105 date "2019-07-01" @default.
• W2979556105 modified "2023-10-16" @default.
• W2979556105 title "Low-dose CT Denoising Using Edge Detection Layer and Perceptual Loss" @default.
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• W2979556105 doi "https://doi.org/10.1109/embc.2019.8857940" @default.
• W2979556105 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31947270" @default.
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