Virtual Coronary Intervention

Objectives This study sought to assess the ability of a novel virtual coronary intervention (VCI) tool based on invasive angiography to predict the patient’s physiological response to stenting. Background Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is associated with improved clinical and economic outcomes compared with angiographic guidance alone. Virtual (v)FFR can be calculated based upon a 3-dimensional (3D) reconstruction of the coronary anatomy from the angiogram, using computational fluid dynamics (CFD) modeling. This technology can be used to perform virtual stenting, with a predicted post-PCI FFR, and the prospect of optimized treatment planning. Methods Patients undergoing elective PCI had pressure-wire–based FFR measurements pre- and post-PCI. A 3D reconstruction of the diseased artery was generated from the angiogram and imported into the VIRTUheart workflow, without the need for any invasive physiological measurements. VCI was performed using a radius correction tool replicating the dimensions of the stent deployed during PCI. Virtual FFR (vFFR) was calculated pre- and post-VCI, using CFD analysis. vFFR pre- and post-VCI were compared with measured (m)FFR pre- and post-PCI, respectively. Results Fifty-four patients and 59 vessels underwent PCI. The mFFR and vFFR pre-PCI were 0.66 ± 0.14 and 0.68 ± 0.13, respectively. Pre-PCI vFFR deviated from mFFR by ±0.05 (mean Δ = −0.02; SD = 0.07). The mean mFFR and vFFR post-PCI/VCI were 0.90 ± 0.05 and 0.92 ± 0.05, respectively. Post-VCI vFFR deviated from post-PCI mFFR by ±0.02 (mean Δ = −0.01; SD = 0.03). Mean CFD processing time was 95 s per case. Conclusions The authors have developed a novel VCI tool, based upon the angiogram, that predicts the physiological response to stenting with a high degree of accuracy.

and the vFFR to be recalculated. The ability to predict the physiological response to a variety of potential stenting strategies would be advantageous in terms of interventional planning.
The aim of this project, therefore, was to develop and validate a system capable of predicting the physiological response to a planned PCI based solely upon coronary angiographic images. mg/kg/min. The FFR value was measured during stable hyperemia. The decision to proceed to PCI was made by the operator, using the findings from angiographic and invasive FFR assessments. The PCI procedure, including determining the number and sizes of stents, followed standard practice. Following PCI, a repeat FFR measurement was recorded.
3D RECONSTRUCTION. A 3D reconstruction of the coronary anatomy was created offline at the end of the procedure using a Philips 3D workstation. Two clear orthogonal planes from similar phases of the cardiac cycle, as close to 90 apart as possible, were selected to segment and reconstruct coronary arterial geometry. The electrocardiography trace was imported alongside the angiographic images, allowing images from end-diastole to be selected. The 3D reconstruction was exported from the workstation as a virtual reality modeling language (i.e., *vrml) file to our VIRTUheart workflow (4).

SIMULATED STENT PLACEMENT AND vFFR CALCULATION.
The simulated stent placement was carried out offline within the VIRTUheart workflow environment, which replicated the dimensions and position of the stent(s) used during the PCI procedure. The geometry of the patient vessel is expressed as a set of connected circular cross sections, following the points formed in the center of the vessel path. Using the dedicated VIRTUheart graphical user interface, the operator marks the arterial location where they wish to deploy a stent ( Figure 1A). The operator then determines the diameter and length of the stent they wish to deploy, just as they would in the cardiac catheter laboratory.
Vessel-stent interaction is simulated by smoothing the vessel trajectory, using a cubic spline and adjusting the cross-sectional radius. The VIRTUheart software then outputs the corrected surface mesh; the virtually stented artery ( Figure 1B). The final vessel geometry is composed of triangle strips connecting each crosssection, each strip containing 128 triangles. This step can be repeated if more than 1 virtual stent is to be inserted in the same artery. This permits the modeling of multiple stent strategies. The ultimate aim of this work will be that operators can compare the physiological impact of different stenting strategies before they treat a patient, so they can select the optimum approach. However, for this validation study, we compared the computed physiological result with the actual physiological result. It was therefore critical that we based the CFD simulation upon matching the virtual stent to the stent actually deployed in the cardiac catheterization laboratory.
A new surface mesh of the altered geometry was created which was then discretized (meshed) into w1  In total, 59 vessels were treated (31 were left anterior descending, 7 were left circumflex, and 21 were right coronary arteries). One patient had no pre-PCI FFR because we were unable to pass the wire, giving 58 paired pre-PCI datasets (Supplemental Figure 2). In 1 case, 2 stents were inserted sequentially with an FFR measurement taken after each, giving 60 paired post-PCI datasets (Supplemental Table 1  The mean difference (bias) between mFFR and vFFR was À0.02 AE 0.07. The average error was AE0.05 (AE5%).
A Bland-Altman plot is shown in Figure 3A.  Figure 3B. The vFFR and mFFR were closely correlated (r ¼ 0.80) ( Figure 4B).  Figure 1A). For a Gosling et al. which is not amenable to vasodilation, so assessment of a proximal lesion underestimates its functional significance (12). Only by removing a stenosis (physically or with our system, virtually) is it possible to increase hyperemic flow. This is often the strategy used in FFR-guided PCI, whereby the operator will  (12). In contrast, by using our VCI tool, the operator can "remove" each stenosis in turn to assess the true impact of each individual

DISCUSSION
lesion. An example of our VCI tool being used to assess tandem lesions in this way is shown in Figure 5.