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10.1364/BOE.8.000679 http://scihub22266oqcxt.onion/10.1364/BOE.8.000679 C5330597!5330597 !28270976     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28270976 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28270976 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biomed+Opt+Express 2017 ; 8 (2 ): 679-694 Nephropedia Template TP {{tp|p=28270976 |t=2017. Low-dose CT via convolutional neural network |pdf=|usr=}}{{28270976 }} gab.com Text Low-dose CT via convolutional neural network JO: Biomed Opt Express http://www.kidney.de/pget.php?&tw=1&pmid=28270976 #FOAvip 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. Twit Text FOAVip Low-dose CT via convolutional neural network ????Biomed Opt Express http://www.kidney.de/pget.php?&tw=1&pmid=28270976 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28270976 #FOAvip English Wikipedia <ref>{{Cite pmid|28270976 }}</ref> Low-dose CT via convolutional neural network #MMPMID28270976 Chen H ; Zhang Y ; Zhang W ; Liao P ; Li K ; Zhou J ; Wang G Biomed Opt Express 2017[Feb]; 8 (2 ): 679-694 PMID28270976 show ga 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. äLow-dose CT via convolutional neural network (epmc).pdf DeepDyve Pubget Overpricing