Low-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 than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.

DOI: 10.1364/BOE.8.000679

Cite this paper

@article{Chen2017LowdoseCV, title={Low-dose CT via 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={Biomedical optics express}, year={2017}, volume={8 2}, pages={679-694} }