Open AccessaLow-dose CT via convolutional neural network
Author(s) -
Hu Chen,
Yi Zhang,
Weihua Zhang,
Peixi Liao,
Ké Li,
Jiliu Zhou,
Ge Wang
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.000679
Subject(s) - convolutional neural network , computer science , image quality , artificial intelligence , noise reduction , projection (relational algebra) , reduction (mathematics) , iterative reconstruction , radiation dose , pattern recognition (psychology) , deep learning , noise (video) , artifact (error) , computer vision , image (mathematics) , nuclear medicine , algorithm , mathematics , medicine , geometry
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.