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Articles Published Processes
9/10/2021 7:23:16 AM | Browse: 628 | Download: 1627
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Received |
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2021-03-16 14:28 |
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Peer-Review Started |
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2021-03-16 14:32 |
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First Decision by Editorial Office Director |
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2021-04-24 03:18 |
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Return for Revision |
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2021-04-24 03:18 |
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Revised |
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2021-05-12 15:41 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2021-08-17 03:18 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2021-08-17 13:23 |
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Articles in Press |
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2021-08-17 13:23 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2021-09-06 06:54 |
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Publish the Manuscript Online |
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2021-09-10 07:23 |
| ISSN |
2307-8960 (online) |
| Open Access |
eviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Editorial |
| Article Title |
Advances in deep learning for computed tomography denoising
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Sung Bin Park |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Sung Bin Park, MD, PhD, Chief Physician, Full Professor, Department of Radiology, Chung-Ang University Hospital, 102, Heukseok-ro, Dongjak-gu, Seoul 06973, South Korea. pksungbin@paran.com |
| Key Words |
Denoising; Deep learning; Computer-assisted imaging processing; Iterative reconstruction; Radiation dose |
| Core Tip |
Early application of deep learning techniques have shown success in the denoising of computed tomography (CT) images, especially low-dose CT images, and future advances are expected to provide additional benefit. |
| Publish Date |
2021-09-10 07:23 |
| Citation |
Park SB. Advances in deep learning for computed tomography denoising. World J Clin Cases 2021; 9(26): 7614-7619 |
| URL |
https://www.wjgnet.com/2307-8960/full/v9/i26/7614.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v9.i26.7614 |
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