A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data-Reference-Cited by-同舟云学术

A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data

Published:2023-06-30 Issue:12 Volume:33 Page:8477-8487
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Chen Bing-Hua^ORCID,Wu Chong-Wen,An Dong-Aolei,Zhang Ji-Lei,Zhang Yi-Hong,Yu Ling-Zhan,Watson Kennedy,Wesemann Luke,Hu Jiani,Chen Wei-Bo,Xu Jian-Rong,Zhao Lei,Feng ChaoLu,Jiang Meng,Pu Jun,Wu Lian-Ming^ORCID

Funder

National Natural Science Foundation of China

National Natural Science Foundation of China Youth project

Shanghai Science and technology innovation action plan, technology standard project Grant numbers

Shanghai Science and technology innovation action plan, technology standard project

Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant: Shanghai Jiao Tong University school of medicine Double hundred outstanding person projrect

Shanghai Jiao Tong University medical engineering cross project

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-023-09807-6.pdf

Reference36 articles.

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5. Meizlish J, Berger H, Plankey M, Errico D, Levy W, Zaret B (1984) Functional left ventricular aneurysm formation after acute anterior transmural myocardial infarction. Incidence, natural history, and prognostic implications. N Engl J Med 311:1001–1006

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