Computational Techniques for Detecting Coronary Atherosclerosis

Coronary atherosclerosis is one of the leading cause of mortality in developed countries, and is increasingly diagnosed via X-ray computed tomography. Due to the large resulting volume of data, recent research has been directed towards developing automated methods of screening CT scans for coronary atherosclerosis. This task typically consists of lumen extraction, plaque detection, plaque quantification, and material discrimination. In this paper, we describe a novel set of techniques for accomplishing the first three steps, which aim to provide higher precision than previous efforts. We also discuss how such a high-precision detection and quantification system could be used to significantly improve on the state of the art in material discrimination. Our methods extract lumen for 71.2% of centreline points, detect plaque with a detection sensitivity of 67% on CTA reference data, and quantify plaque with a linear weighted kappa coefficient of 0.08.MAS