aLow-dose CT via convolutional neural network

In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM…

• NN
  National Natural Science Foundation of China

  Awards: 61302028, 61671312, 61202160
• NI
  National Institutes of Health
• NI
  National Institute of Biomedical Imaging and Bioengineering

  Awards: R01 EB016977, U01 EB017140