| For: | 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 [PMID: 40741013 DOI: 10.4330/wjc.v17.i7.108745] |
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| URL: | https://www.wjgnet.com/1949-8462/full/v17/i7/108745.htm |
| Number | Citing Articles |
| 1 |
Jadranka Stojanovska, Mahesh B. Keerthivasan, Samantha Platt, Kana Fujikura. Cardiac Magnetic Resonance Scan Efficiency. Magnetic Resonance Imaging Clinics of North America 2026; 34(2) doi: 10.1016/j.mric.2026.01.002
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| 2 |
Sadegh Dehghani, Ali Rajabi, Amirhossein Rahmati, Negar Omidi. Optimizing Cine Cardiac MRI: Technical Advances and Clinical Applications. Journal of Magnetic Resonance Imaging 2026; 64(1) doi: 10.1002/jmri.70310
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| 3 |
Katerina Eyre, Kenan Kaya, Thomas Coudert, Christopher W. Roy, Jérôme Yerly, Dinghui Wang, Isabel Monton Quesada, Augustin C. Ogier, Ruud B. van Heeswijk, Marco Müller, Jouke Smink, Omer Burak Demirel, Robert J. Holtackers, Kim-Lien Nguyen, Tim Leiner, Matthias Stuber. Assessment and validation of the established free-running framework for cardiac function by magnetic resonance imaging at 1.5T (FAST-CMR): an international multi-center, multi-vendor study. Journal of Cardiovascular Magnetic Resonance 2026; doi: 10.1016/j.jocmr.2026.102784
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