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Non-calcified plaque (NCP) detection in coronary CT angiography (cCTA) is challenging due to the low CT number of NCP, the large number of coronary arteries and multiple phase CT acquisition. We are developing computer-vision methods for automated detection of NCPs in cCTA. A data set of 62 cCTA scans with 87 NCPs was collected retrospectively from patient files. Multiscale coronary vessel enhancement and rolling balloon tracking were first applied to each cCTA volume to extract the coronary artery trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for both positive and negative remodeling candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. A quantitative luminal analysis was newly designed for feature extraction and false positive (FP) reduction. We extracted 9 geometric features and 6 gray-level features, to quantify the differences between NCPs and FPs. The gray-level features included 4 features to measure local statistical characteristics and 2 asymmetry features to measure the asymmetric spatial location of gray-level density along the vessel centerline. The geometric features included a radius differential feature and 8 features extracted from two transformed volumes: the volumetric shape indexing and the gradient direction mapping volumes. With a machine learning algorithm and feature selection method, useful features were selected and combined into an NCP likelihood measure to differentiate TPs from FPs. With the NCP likelihood measure as a decision variable in the receiver operating characteristic (ROC) analysis, the area under the curve achieved a value of 0.85±0.01, indicating that the luminal analysis is effective in reducing FPs for NCP detection.
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