"Virtual" (Computed) Fractional Flow Reserve: Current Challenges and Limitations

Abstract

Fractional flow reserve (FFR) is the "gold standard" for assessing the physiological significance of coronary artery disease during invasive coronary angiography. FFR-guided percutaneous coronary intervention improves patient outcomes and reduces stent insertion and cost; yet, due to several practical and operator related factors, it is used in <10% of percutaneous coronary intervention procedures. Virtual fractional flow reserve (vFFR) is computed using coronary imaging and computational fluid dynamics modeling. vFFR has emerged as an attractive alternative to invasive FFR by delivering physiological assessment without the factors that limit the invasive technique. vFFR may offer further diagnostic and planning benefits, including virtual pullback and virtual stenting facilities. However, there are key challenges that need to be overcome before vFFR can be translated into routine clinical practice. These span a spectrum of scientific, logistic, commercial, and political areas. The method used to generate 3-dimensional geometric arterial models (segmentation) and selection of appropriate, patient-specific boundary conditions represent the primary scientific limitations. Many conflicting priorities and design features must be carefully considered for vFFR models to be sufficiently accurate, fast, and intuitive for physicians to use. Consistency is needed in how accuracy is defined and reported. Furthermore, appropriate regulatory and industry standards need to be in place, and cohesive approaches to intellectual property management, reimbursement, and clinician training are required. Assuming successful development continues in these key areas, vFFR is likely to become a desirable tool in the functional assessment of coronary artery disease.

Keywords: computational fluid dynamics; computational modeling; coronary angiography; fractional flow reserve; percutaneous coronary intervention; virtual fractional flow reserve.

Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1

A vFFR Model Workflow Coronary angiogram (A) is “segmented” and reconstructed (B) into a 3-dimensional (3D) model (C). Surface and volumetric meshing “discretize” the patient-specific geometry (C). The physiological conditions beyond the modeled section must be represented at each boundary, that is, “boundary conditions” (D). Computational fluid dynamics simulation computes the pressure gradient, using the anatomical 3D model “tuned” with physiological parameters. Pressure ratio is computed from output data (E). Results are validated against invasive measurements during development (F). vFFR = virtual fractional flow reserve.

Figure 2

vFFR Virtual Pullback Result Pressure distribution throughout a right coronary artery (RCA) allowing individual lesion evaluation. vCFR = virtual coronary flow reserve; vFFR = virtual fractional flow reserve.

Figure 3

Electrical Model Demonstrating the Importance of the Distal Boundary During vFFR Computation Pressure (P), flow, and resistance (R) are analogous to electrical potential difference, current, and resistance. Rstenosis and coronary microvasculature circulation resistance (RCMVC) are effectively 2 resistors arranged in series. Therefore, even if Paorta and Rstenosis are known (or computed), computation of virtual fractional flow reserve (vFFR) is wholly dependent on the parameters of RCMVC, because this determines Pdistal and, hence, vFFR. If CMVC resistance is overestimated, lesion severity and the potential benefit from revascularization are underestimated, that is, vFFR > fractional flow reserve, and vice versa.

Figure 4

Modeling the distal coronary boundary The 3-dimensional (3D) coronary model is coupled to a 0-dimensional, lumped parameter model to represent the physiological conditions at the distal boundary (right). An electrically analogous Windkessel design represents the impedance (Z), resistance (R), and capacitance (C) of the distal coronary microvasculature circulation. The algebraically coded Windkessel computes pressure (P) and flow (Q), which dynamically informs the 3D computational fluid dynamics simulation. As in Figure 3, model parameters are vital to patient-specific accuracy.