Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis.

By Charles A. Taylor, T. Fonte, James K. Min. Journal of the American College of Cardiology, 2013

1,159 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging, Surgery

View Paper: https://www.semanticscholar.org/paper/e2bd8428ee7f0b2ee84f3553443f203b6322d141

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Lucian Mihai Itu
2. Bogdan Georgescu
3. Saikiran Rapaka
4. Dorin Comaniciu
5. Tiziano Passerini
6. B. Nørgaard
7. S. Achenbach
8. J. Leipsic

Citations in the corpus

• Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). (2014, positive): “The improved diagnostic performance of FFRCT in the present study, in particular with regard to specificity, importantly reflects substantial refinements in FFRCT technology and physiological modeling (14,17), as well as an increased focus on CT image quality (17) and adherence to official recommend”
• A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. (2016, background): “This approach leads to a large number of algebraic equations, making it computationally very demanding (48).”