A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR). Issue 3 (7th February 2018) - Freiman, Moti; Nickisch, Hannes; Schmitt, Holger; Maurovich‐Horvat, Pál; Donnelly, Patrick M; Vembar, Mani; Goshen, Liran
Abstract : Purpose: The purpose of this study is to develop and evaluate a functionally personalized boundary condition (BC) model for estimating the fractional flow reserve (FFR) from coronary computed tomography angiography (CCTA) using flow simulation (CT‐FFR). Materials and methods: The CCTA data of 90 subjects with subsequent invasive FFR in 123 lesions within 21 days (range: 0–83) were retrospectively collected. We developed a functionally personalized BC model accounting specifically for the coronary microvascular resistance dependency on the coronary outlets pressure suggested by several physiological studies. We used the proposed model to estimate the hemodynamic significance of coronary lesions with an open‐loop physics‐based flow simulation. We generated three‐dimensional (3D) coronary tree geometries using automatic software and corrected manually where required. We evaluated the improvement in CT‐FFR estimates achieved using a functionally personalized BC model over anatomically personalized BC model using k‐fold cross‐validation. Results: The functionally personalized BC model slightly improved CT‐FFR specificity in determining hemodynamic significance of lesions with intermediate diameter stenosis (30%–70%, N = 72), compared to the anatomically personalized model lesions with invasive FFR measurements as the reference (sensitivity/specificity: 0.882/0.79 vs 0.882/0.763). For the entire set of 123 coronary lesions, the functionally personalized BC model improved only the area under the curve (AUC) but not the sensitivity/specificity in determining the hemodynamic significance of lesions, compared to the anatomically personalized model (AUC: 0.884 vs 0.875, sensitivity/specificity: 0.848/0.805). Conclusion: The functionally personalized BC model has the potential to improve the quality of CT‐FFR estimates compared to an anatomically personalized BC model.