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.
@article{arxiv.1610.00321,
title = {Low-dose CT denoising with convolutional 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:1610.00321},
year = {2016}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1609.08508