Visual analysis of three-dimensional (3D) Coronary Computed Tomography Angiography (CCTA) remains challenging
due to large number of image slices and tortuous character of the vessels. We aimed to develop an accurate, automated
algorithm for detection of significant and subtle coronary artery lesions compared to expert interpretation. Our
knowledge-based automated algorithm consists of centerline extraction which also classifies 3 main coronary arteries
and small branches in each main coronary artery, vessel linearization, lumen segmentation with scan-specific lumen
attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass
algorithm which considers expected or "normal" vessel tapering and luminal stenosis from the segmented vessel.
Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and
mid segments (67%) of the coronary artery, considering small branch locations. We applied this algorithm to 21 CCTA
patient datasets, acquired with dual-source CT, where 7 datasets had 17 lesions with stenosis greater than or equal to
25%. The reference standard was provided by visual and quantitative identification of lesions with any ≥25% stenosis by
an experienced expert reader. Our algorithm identified 16 out of the 17 lesions confirmed by the expert. There were 16
additional lesions detected (average 0.13/segment); 6 out of 16 of these were actual lesions with <25% stenosis. On persegment
basis, sensitivity was 94%, specificity was 86% and accuracy was 87%. Our algorithm shows promising results
in the high sensitivity detection and localization of significant and subtle CCTA arterial lesions.