Frontiers in Bioengineering and Biotechnology (Aug 2023)
A novel method for noninvasive quantification of fractional flow reserve based on the custom function
Abstract
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., FFRCT). However, transient numerical simulation-based FFRCT 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 FFRCT 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 FFRCT calculation (FFRU). We tested the diagnostic accuracy of FFRU calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFRD)) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFRU calculation was greatly reduced from 1–4 h for transient numerical simulations to 5 min per simulation, which was 2-fold less than the FFRD method. According to the results of the Bland-Altman analysis, the consistency between FFRU and invasive FFR of 86 vessels was better than that of FFRD. The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFRD and FFRU 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 FFRD, and 0.92 (95% CI: 0.89–0.96) for FFRU. The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFRCT calculation, and support its wide application as a diagnostic indicator in clinical practice.
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