Automated epicardial fat volume quantification from non-contrast CT

Abstract

Epicardial fat volume (EFV) is now regarded as a significant imaging biomarker for cardiovascular risk strat-ification. Manual or semi-automated quantification of EFV includes tedious and careful contour drawing of pericardium on fine image features. We aimed to develop and validate a fully-automated, accurate algorithm for EVF quantification from non-contrast CT using active contours and multiple atlases registration. This is a knowledge-based model that can segment both the heart and pericardium accurately by initializing the location and shape of the heart in large scale from multiple co-registered atlases and locking itself onto the pericardium actively. The deformation process is driven by pericardium detection, extracting only the white contours repre- senting the pericardium in the CT images. Following this step, we can calculate fat volume within this region (epicardial fat) using standard fat attenuation range. We validate our algorithm on CT datasets from 15 patients who underwent routine assessment of coronary calcium. Epicardial fat volume quantified by the algorithm (69.15 &plusmn; 8.25 cm³) and the expert (69.46 &plusmn; 8.80 cm³) showed excellent correlation (<i>r</i> = 0.96, <i>p</i> &lt; 0.0001) with no significant differences by comparison of individual data points (<i>p</i> = 0.9). The algorithm achieved a Dice overlap of 0.93 (range 0.88 - 0.95). The total time was less than 60 sec on a standard windows computer. Our results show that fast accurate automated knowledge-based quantification of epicardial fat volume from non-contrast CT is feasible. To our knowledge, this is also the first fully automated algorithms reported for this task.