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Articles Published Processes
7/22/2025 8:45:00 AM | Browse: 148 | Download: 590
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Received |
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2025-04-24 06:43 |
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Peer-Review Started |
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2025-04-24 06:44 |
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First Decision by Editorial Office Director |
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2025-05-22 11:39 |
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Return for Revision |
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2025-05-22 11:39 |
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Revised |
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2025-06-04 02:55 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-07-01 02:46 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-07-01 08:29 |
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Articles in Press |
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2025-07-01 08:29 |
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Edit the Manuscript by Language Editor |
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2025-07-08 19:59 |
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Typeset the Manuscript |
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2025-07-18 00:14 |
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Publish the Manuscript Online |
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2025-07-22 08:45 |
| ISSN |
1949-8462 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. 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 |
Cardiac & Cardiovascular Systems |
| Manuscript Type |
Prospective Study |
| Article Title |
Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Huaijun Wang, Anne Schmieder, Mary Watkins, Pengjun Wang, Joshua Mitchell, S Zyad Qamer and Gregory Lanza |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| James Russell Hornsby and Jun Xiong Fund and United Imaging Healthcare |
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| Corresponding Author |
Gregory Lanza, MD, PhD, Division of Cardiology, Washington University in Saint Louis, Cortex One Building 4320 Forest Park Ave, Saint Louis, WA 63108, United States. gmlanza@wustl.edu |
| Key Words |
Cardiac magnetic resonance; CINE imaging; artificial intelligence; Compressed sensing; Imaging workflow; Acquisition time; Cardiac function; Cardio-oncology; Image quality; Challenging patients |
| Core Tip |
In this prospective study of 89 patients and volunteers, we demonstrate that artificial-intelligence-assisted compressed sensing (AI-CS-CINE) significantly streamlines cardiac magnetic resonance imaging workflows, reducing acquisition time by 84% (37 seconds vs 238 seconds) compared to conventional CINE imaging. Quantitative analysis showed excellent agreement in biventricular volumes and function (intraclass correlation coefficient 0.73-0.98). Importantly, AI-CS-CINE proved especially valuable in challenging cases, such as patients with cardiac amyloidosis, enabling faster acquisition and more reliable interpretation. These findings highlight AI-CS-CINE as a robust, time-efficient alternative to conventional methods, with potential to improve clinical efficiency and image quality in diverse cardiac populations. |
| Publish Date |
2025-07-22 08:45 |
| Citation |
Wang H, Schmieder A, Watkins M, Wang P, Mitchell J, Qamer SZ, Lanza G. Artificial intelligence-assisted compressed sensing CINE enhances the workflow of cardiac magnetic resonance in challenging patients. World J Cardiol 2025; 17(7): 108745
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| URL |
https://www.wjgnet.com/1949-8462/full/v17/i7/108745.htm |
| DOI |
https://dx.doi.org/10.4330/wjc.v17.i7.108745 |
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