A novel method for noninvasive quantification of fractional flow reserve based on the custom function.

Boundary condition settings are key risk factors for the accuracy of noninvasive quantification of fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFR). However, transient numerical simulation-based FFR often ignores the three-dimensional (3D) model of coronary artery and clinical statistics of hyperemia state set by boundary conditions, resulting in insufficient computational accuracy and high computational cost. Therefore, it is necessary to develop the custom function that combines the 3D model of the coronary artery and clinical statistics of hyperemia state for boundary condition setting, to accurately and quickly quantify FFR under steady-state numerical simulations. The 3D model of the coronary artery was reconstructed by patient computed tomography angiography (CTA), and coronary resting flow was determined from the volume and diameter of the 3D model. Then, we developed the custom function that took into account the interaction of stenotic resistance, microcirculation resistance, inlet aortic pressure, and clinical statistics of resting to hyperemia state due to the effect of adenosine on boundary condition settings, to accurately and rapidly identify coronary blood flow for quantification of FFR calculation (FFR). We tested the diagnostic accuracy of FFR calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFR)) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFR calculation was greatly reduced from 1-4 h for transient numerical simulations to 5 min per simulation, which was 2-fold less than the FFR method. According to the results of the Bland-Altman analysis, the consistency between FFR and invasive FFR of 86 vessels was better than that of FFR. The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFR and FFR at the lesion level were 0.62 (95% CI: 0.51-0.74), 0.67 (95% CI: 0.56-0.79), 0.85 (95% CI: 0.76-0.94), and 0.93 (95% CI: 0.87-0.98), respectively. At the patient level, the AUC was 0.61 (95% CI: 0.48-0.74) for CTA, 0.65 (95% CI: 0.53-0.77) for QCA, 0.83 (95% CI: 0.74-0.92) for FFR, and 0.92 (95% CI: 0.89-0.96) for FFR. The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFR calculation, and support its wide application as a diagnostic indicator in clinical practice.