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10.1093/jrr/rry019 http://scihub22266oqcxt.onion/10.1093/jrr/rry019 C6054223!6054223!29659997     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29659997&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 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Radiat+Res 2018 ; 59 (4): 501-10 Nephropedia Template TP {{tp|p=29659997|t=2018. Image quality improvement in cone-beam CT using the super-resolution technique |pdf=|usr=}}{{29659997}} gab.com Text Image quality improvement in cone-beam CT using the super-resolution technique JO: J Radiat Res http://www.kidney.de/pget.php?&tw=1&pmid=29659997 #FOAvip This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality. Twit Text FOAVip Image quality improvement in cone-beam CT using the super-resolution technique ????J Radiat Res http://www.kidney.de/pget.php?&tw=1&pmid=29659997 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29659997 #FOAvip English Wikipedia <ref>{{Cite pmid|29659997}}</ref> Image quality improvement in cone-beam CT using the super-resolution technique #MMPMID29659997 Oyama A; Kumagai S; Arai N; Takata T; Saikawa Y; Shiraishi K; Kobayashi T; Kotoku J J Radiat Res 2018[Jul]; 59 (4): 501-10 PMID29659997show ga This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality. äImage quality improvement in cone-beam CT using the super-resolution t(epmc).pdf DeepDyve Pubget Overpricing