Fast Virtual Fractional Flow Reserve Based Upon Steady-State Computational Fluid Dynamics Analysis

Visual Abstract


SUMMARY
Fractional flow reserve (FFR)-guided percutaneous intervention is superior to standard assessment but remains underused. The authors have developed a novel "pseudotransient" analysis protocol for computing virtual fractional flow reserve (vFFR) based upon angiographic images and steady-state computational fluid dynamics. This protocol generates vFFR results in 189 s (cf >24 h for transient analysis) using a desktop PC, with <1% error relative to that of full-transient computational fluid dynamics analysis. Sensitivity analysis demonstrated that physiological lesion significance was influenced less by coronary or lesion anatomy (33%) and more by microvascular physiology (59%). If coronary microvascular resistance can be estimated, vFFR can be accurately computed in less time than it takes to make invasive measurements.   (1,2). When FFR is used to guide percutaneous coronary intervention (PCI), clinical outcomes are improved, fewer stents are deployed, and costs are reduced (3)(4)(5). However, even in countries where FFR is most frequently used, FFR is used in < 10% of PCI procedures and far fewer diagnostic cases (6,7). This is due to a combination of factors related to practicality, time, and cost. Using computational fluid dynamics (CFD) to compute a "virtual" FFR The outputs of any model are determined by variations in input parameters which may occur due to natural biological variability or error in measurement.
In the context of vFFR, these errors include a variety of geometric and physiological parameters. Promising vFFR results have been produced despite limitations in coronary imaging and segmentation and in the ways in which physiological parameters are used in model tuning (9,14). It is important to understand the relative sensitivity of computed FFR to individual model input parameters. Sensitivity analysis is a formal mathematical process which allows the influence and interdependencies of individual model inputs to be decomposed and quantified in terms of their effects on model outputs, which in this case is the vFFR result.  Angiograms of a diseased right coronary artery (left) have been segmented, and the reconstructed vessel is shown (middle) alongside the processed pressure data (right) within the VIRTUheart workflow environment.

FIGURE 2 Sample Finite Element Mesh Used for Simulations
Mesh shown is produced from the angiogram shown in Figure 1. Details of the wall (blue) and inlet (green) are shown. The near-wall region is refined using prism elements.  The imaging and pressure input data for both novel models are those collected during routine coronary angiography (image data in yellow and aortic pressure data in green). The parameters of CMV physiology must be estimated (red). The type of simulation used to calculate vFFR values are shown in the blue boxes. vFFR ps-trns is a function of 9 parameters, whereas vFFR steady 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 ¼ compliance; Morris et al.  Table 1. In total, 73 unique arterial datasets were studied ( Table 2), which Values are mean (range), %, or mean (%).
ACE ¼ angiotensin-converting enzyme; ARB ¼ angiotensin receptor blocker.  Figure 5A. Agreement between vFFR ps-trns and measured data was also high (   ACCURACY OF vFFR steady . Agreement between vFFR steady and measured FFR was also high ( Table 2).
A Bland-Altman plot is shown in Figure 5B.     Table 3. Accuracy improved as the averaged value for CMV applied at the distal boundary better and more specifically reflected the coronary arterial subgrouping. approach has previously been demonstrated to be acceptable in coronary hemodynamic modeling (22) and appears appropriate in the current study.  Values are mean AE SD unless otherwise indicated.

DISCUSSION
CMVRtotal ¼ total coronary microvascular resistance; DX ¼ diagonal artery; LAD ¼ left anterior descending artery; LCX ¼ left circumflex artery; LMS ¼ left main coronary artery; RCA ¼ right coronary artery; other abbreviations as in Table 2.  However, this study demonstrates that when vFFR is computed, geometric precision is of secondary importance to the precision of the CMV resistance. Furthermore, the methods developed in this study are applicable to any coronary segmentation. Fourth, the current sensitivity analysis examines the sensitivity of the model to interpatient variability (leading to variability in vFFR prediction). The variation of all vFFR values has been decomposed and attributed to the individual model parameters (or combinations of these parameters). This study did not address intrapatient sensitivity and uncertainty of vFFR predictions due to measurement uncertainties. This is something we intend to develop for future iterations of the vFFR workflow and will be formally examined on a patient-by-patient basis.