Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer


Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects causing by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while changing the complexity of the network, minimally.

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This work was supported in part by a research grant from Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to thank Dr. Paul Babyn and Troy Anderson for the acquisition of the piglet dataset. The results shown here are in whole or part based upon data generated by the TCGA Research Network:


This work was supported in part by a research grant from Natural Sciences and Engineering Research Council of Canada (NSERC).

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Correspondence to Javad Alirezaie.

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Gholizadeh-Ansari, M., Alirezaie, J. & Babyn, P. Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer. J Digit Imaging 33, 504–515 (2020).

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  • Low-dose CT image
  • Dilated convolution
  • Deep neural network
  • Noise removal
  • Perceptual loss
  • Edge detection