The determination of hemodynamic significance of coronary artery lesions from cardiac computed tomography angiography (CCTA) based on blood 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 effect (PVE) in small-diameter lumina 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 PVE. Our algorithm detects lumen regions that may be subject to the PVE by analyzing the intensity values along the coronary centerline and integrates this information into a machine-learning based graph min-cut segmentation framework to obtain accurate coronary lumen segmentations. We demonstrate the improvement in hemodynamic significance assessment achieved by accounting for the PVE in the automatic segmentation of 91 coronary artery lesions from 85 patients. We compare hemodynamic significance assessments by means of fractional flow reserve (FFR) resulting from simulations on 3D models generated by our segmentation algorithm with and without accounting for the PVE. By accounting for the PVE we improved the area under the ROC 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.
An in-vivo feasibility study of potentially improved atherosclerosis CT imaging is presented. By administration of two
different contrast agents to rabbits with induced atherosclerotic plaques we aim at identifying both soft plaque and vessel
lumen simultaneously. Initial injection of iodinated nanoparticle (INP) contrast agent (N1177 - Nanoscan Imaging), two
to four hours before scan, leads to its later accumulation in macrophage-rich soft plaque, while a second gadolinium
contrast agent (Magnevist) injected immediately prior to the scan blends with the aortic blood. The distinction between
the two agents in a single scan is achieved with a double-layer dual-energy MDCT (Philips Healthcare) following
material separation analysis using the reconstructed images of the different x-ray spectra. A single contrast agent
injection scan, where only INP was injected two hours prior to the scan, was compared to a double-contrast scan taken
four hours after INP injection and immediately after gadolinium injection. On the single contrast agent scan we observed
along the aorta walls, localized iodine accumulation which can point on INP uptake by atherosclerotic plaque. In the
double-contrast scan the gadolinium contributes a clearer depiction of the vessel lumen in addition to the lasting INP
presence. The material separation shows a good correlation to the pathologies inferred from the conventional CT images
of the two different scans while performing only a single scan prevents miss-registration problems and reduces radiation
dose. These results suggest that a double-contrast dual-energy CT may be used for advanced clinical diagnostic
applications.
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