Abstract
Coronary plaque burden measured by coronary computerized tomography angiography (CCTA), independent of stenosis, is a significant independent predictor of coronary heart disease (CHD) events and mortality. Hence, it is essential to develop comprehensive CCTA plaque quantification beyond existing subjective plaque volume or stenosis scoring methods. The purpose of this study is to develop a framework for automated 3D segmentation of CCTA vessel wall and quantification of atherosclerotic plaque, independent of the amount of stenosis, along with overcoming challenges caused by poor contrast, motion artifacts, severe stenosis, and degradation of image quality. Vesselness, region growing, and two sequential level sets are employed for segmenting the inner and outer wall to prevent artifact-defective segmentation. Lumen and vessel boundaries are joined to create the coronary wall. Curved multiplanar reformation is used to straighten the segmented lumen and wall using lumen centerline. In-vivo evaluation included CCTA stenotic and non-stenotic plaques from 41 asymptomatic subjects with 122 plaques of different characteristics against the individual and consensus of expert readers. Results demonstrate that the framework segmentation performed robustly by providing a reliable working platform for accelerated, objective, and reproducible atherosclerotic plaque characterization beyond subjective assessment of stenosis; can be potentially applicable for monitoring response to therapy.
Highlights
Background and Related WorkMany paradigms were introduced for coronary artery lumen segmentation and stenosis detection from coronary computerized tomography angiography (CCTA) images
The experiments were part of a study that was approved by the local institutional review board (IRB) at the National Institutes of Health, in compliance with the Declaration of Helsinki, and were performed in accordance with relevant guidelines and regulations
While there are multiple studies that addressed the problem of coronary lumen stenosis detection, there are few that attempts to systematically delineate coronary wall from CTA, for asymptomatic Coronary artery disease (CAD)
Summary
Background and Related WorkMany paradigms were introduced for coronary artery lumen segmentation and stenosis detection from CCTA images. Marquering et al.[12] employed a fast marching level set to estimate the initial lumen contour and a model-guided minimum cost approach[13] to find the final contour. Schaap et al.[15] employed the intensities along a given centerline to guide a graph cut algorithm for lumen segmentation. These techniques were limited to specific plaque types, e.g., calcified plaques. They were only demonstrated in a limited number of cases The performance of these techniques was generally sensitive to the initial centerline accuracy, the length of the stenosis, and the artery cross section shape
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