In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imaging quality. In this paper, we propose a noise reduction method for low-dose CT via deep learning without accessing the original projection data. An architecture of deep convolutional neural network was considered to map the low-dose CT images into its corresponding normal-dose CT images patch by patch. Qualitative and quantitative evaluations demonstrate a state-the-art performance of the proposed method.
@article{arxiv.1609.08508,
title = {Low-Dose CT via Deep Neural Network},
author = {Hu Chen and Yi Zhang and Weihua Zhang and Peixi Liao and Ke Li and Jiliu Zhou and Ge Wang},
journal= {arXiv preprint arXiv:1609.08508},
year = {2016}
}