Predictive Physiological Modeling of Percutaneous Coronary Intervention – Is Virtual Treatment Planning the Future?

FIGURE 1. Computational replication of bifurcation stenting. Mortier et al. (2015) compared flow velocity patterns and WSS following virtual provisional stenting (left) and proximal optimization technique (right). (A) Velocity contour maps on an internal plane of the bifurcation at peak flow rate. In the provisional stenting, where the malapposed struts are clearly visible (black arrows), higher velocities are present as the stent behaves as a cylindrical hurdle in the flow stream. Indeed, in the proximal optimization technique (POT), struts are well opposed to the wall and do not obstruct the blood stream. (B) Time-averaged WSS (TAWSS) contour maps. Low WSS are generated next to the stent struts. A wide area with low WSS is present in the bifurcation region in the provisional stenting model and in the proximal part of the stent in the POT model. Reprinted from Mortier et al. (2015). Copyright (2015), with permission from Europa Digital & Publishing.

FIGURE 2. An example of virtual coronary intervention. Angiography revealed a severe mid vessel stenosis in the LAD (arrow). The mFFR between the proximal and distal points marked with the dashed line was 0.77. (B) The angiograms were used to model the vFFR using the VIRTUheart system, which was calculated to be 0.75 over the same segment. This is displayed as a straight yellow line connecting the same two points between which the vFFR was calculated, exactly matching the two spots marked by the dashed line in (A). (C) After implantation of a 2.75 mm × 18 mm stent at the stenosis, the mFFR was 0.88 over the same segment. (D) VCI using the VIRTUheart system was then used to implant a “virtual” 2.75 mm × 18 mm stent, and the recalculated vFFR was 0.88, corresponding to a green color in the line connecting the two points. Reproduced from JACC: Cardiovascular imaging under creative commons license CC BY 4.0 (Gosling et al., 2018).