Low-Dose CT Restoration with Deep Neural Network

Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, Ge Wang


Published in:Fully3D 2017 Proceedings


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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