Automated Agatston score computation in a large dataset of non ECG-gated chest computed tomography

Abstract

The Agatston score, computed from ECG-gated computed tomography (CT), is a well established metric of coronary artery disease. It has been recently shown that the Agatston score computed from chest CT (non ECG-gated) studies is highly correlated with the Agatston score computed from cardiac CT scans. In this work we present an automated method to compute the Agatston score from chest CT images. Coronary arteries calcifications (CACs) are defined as voxels contained within the coronary arteries with a value greater or equal to 130 Hounsfield Units (HU). CACs are automatically detected in chest CT studies by locating the heart, generating a region of interest around it, thresholding the image in such region and applying a set of rules to discriminate CACs from calcifications in the main vessels or from metallic implants. We evaluate the methodology in a large cohort of 1500 patients for whom manual reference standard is available. Our results show that the Pearson correlation coefficient between manual and automated Agatston score is p = 0.86 (p < 0.0001).