High-Speed On-Site Deep Learning–Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard-Reference-Cited by-同舟云学术

High-Speed On-Site Deep Learning–Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard

Published:2023-10 Issue:4 Volume:221 Page:460-470
ISSN:0361-803X
Container-title:American Journal of Roentgenology
language:en
Short-container-title:American Journal of Roentgenology

Author:

Giannopoulos Andreas A.¹,Keller Lukas¹,Sepulcri Daniel¹,Boehm Reto¹,Garefa Chrysoula¹,Venugopal Prem²,Mitra Jhimli²,Ghose Soumya²,Deak Paul²,Pack Jed D.²,Davis Cynthia L.²,Stähli Barbara E.³,Stehli Julia³,Pazhenkottil Aju P.¹,Kaufmann Philipp A.¹,Buechel Ronny R.¹

Affiliation:

1. Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Ramistrasse 100, Zurich 8091, Switzerland.

2. GE Research, Schenectady, NY.

3. Department of Cardiology, University Heart Center, University Hospital Zurich, Zurich, Switzerland.

Publisher

American Roentgen Ray Society

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://www.ajronline.org/doi/pdf/10.2214/AJR.23.29156

Reference39 articles.

1. Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve