Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion-Reference-Cited by-同舟云学术

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion

Published:2022-02-23 Issue:9 Volume:49 Page:3119-3128
ISSN:1619-7070
Container-title:European Journal of Nuclear Medicine and Molecular Imaging
language:en
Short-container-title:Eur J Nucl Med Mol Imaging

Author:

Muscogiuri Giuseppe,Chiesa Mattia,Baggiano Andrea,Spadafora Pierino,De Santis Rossella,Guglielmo Marco,Scafuri Stefano,Fusini Laura,Mushtaq Saima,Conte Edoardo,Annoni Andrea,Formenti Alberto,Mancini Maria Elisabetta,Ricci Francesca,Ariano Francesco Paolo,Spiritigliozzi Luigi,Babbaro Mario,Mollace Rocco,Maragna Riccardo,Giacari Carlo Maria,Andreini Daniele,Guaricci Andrea Igoren,Colombo Gualtiero I.,Rabbat Mark G.,Pepi Mauro,Sardanelli Francesco,Pontone Gianluca^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00259-022-05732-w.pdf

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4. Pontone G, Muscogiuri G, Andreini D, Guaricci AI, Guglielmo M, Mushtaq S, et al. The new frontier of cardiac computed tomography angiography: fractional flow reserve and stress myocardial perfusion. Curr Treat Options Cardiovasc Med. 2016;18:74.

5. Pontone G, Andreini D, Guaricci AI, Baggiano A, Fazzari F, Guglielmo M, et al. Incremental diagnostic value of stress computed tomography myocardial perfusion with whole-heart coverage CT scanner in intermediate- to high-risk symptomatic patients suspected of coronary artery disease. JACC Cardiovasc Imaging. 2019;12:338–49.

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