Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment

The determination of hemodynamic significance of coronary artery lesions from cardiac computed tomography angiography (CCTA) based on flow simulations has the potential to improve CCTA’s specificity, thus resulting in improved clinical decision making. Accurate coronary lumen segmentation required for flow simulation is challenging due to several factors. Specifically, the partial-volume (PV) effect in small-diameter lumen may result in overestimation of the lumen diameter that can lead to an erroneous hemodynamic significance assessment. In this work, we present a coronary artery segmentation algorithm tailored specifically for flow simulations by accounting for the PV effect. Our algorithm detects lumen regions that may be subject to the PV effect by analyzing the coronary centerline intensity profile and integrating this information into a machine-learning based graph min-cut segmentation framework to obtain accurate coronary lumen segmentations. We demonstrated the improvement in hemodynamic significance assessment achieved by accounting for the PV effect in the automatic segmentation of 91 coronary artery lesions from 85 patients. We compared hemodynamic significance assessments by means of fractional flow reserve (FFR) resulted from simulations on 3D models generated by our segmentation algorithm with and without accounting for the PV effect. By accounting for the PV effect we improved the area under the curve for detecting hemodynamically significant CAD by 29% (N=91, 0.85 vs. 0.66, p<0.05, Delong’s test) with invasive FFR threshold of 0.8 as the reference standard. Our algorithm has the potential to facilitate non-invasive hemodynamic significance assessment of coronary lesions.