Keywords:
low-dose CT, noise reduction, deep learning, convolutional neural network
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.
- Hu Chen
- Sichuan University
- Yi Zhang
- Sichuan University
- Weihua Zhang
- Sichuan University
- Peixi Liao
- The Sixth People’s Hospital of Chengdu
- Ke Li
- Sichuan University
- Jiliu Zhou
- Sichuan University
- Ge Wang
- Rensselaer Polytechnic Institute
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