10.25407/JACBTS.8254640.v1
Paul D. Morris
Daniel Alejandro Silva Soto
Jeroen F.A. Feher
Dan Rafiroiu
Angela Lungu
Susheel Varma
Patricia V. Lawford
D. Rodney Hose
Julian P. Gunn
Figure 3: Models for Computing vFFR
2019
JACC: Basic to Translational Science
computational fluid dynamics
coronary artery disease
coronary microvascular physiology
coronary modelling
coronary physiology
fractional flow reserve
virtual fractional flow reserve
2019-06-11 07:04:45
article
https://multimedia.onlinejacc.org/articles/Figure_3_Models_for_Computing_vFFR/8254640
The imaging and pressure input data for both novel models are those collected during routine coronary angiography (image data in <b>yellow</b> and aortic pressure data in <b>green</b>). The parameters of CMV physiology must be estimated <b>(red)</b>. The type of simulation used to calculate vFFR values are shown in the <b>blue boxes</b>. vFFRps-trns is a function of 9 parameters, whereas vFFRsteady is a function of 4. Pseudotransient flow can be reconstructed using a 1D flow model representing the 3D vessel geometry coupled to the 0-dimensional Windkessel model. C = compliance; CMV = coronary microvasculature; R = resistance; vFFR = virtual fractional flow reserve; Z = impedance.