Journal
BIOMEDICAL OPTICS EXPRESS
Volume 8, Issue 2, Pages 679-694Publisher
Optica Publishing Group
DOI: 10.1364/BOE.8.000679
Keywords
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Funding
- National Natural Science Foundation of China (NSFC) [61202160, 61302028, 61671312]
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)/National Institutes of Health (NIH) [R01 EB016977, U01 EB017140]
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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. (C) 2017 Optical Society of America
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