Quantification of Roughness of Calcific Deposits in Computed Tomography Scans of Human Coronary Arteries

Abstract

The incidence of coronary artery disease has been shown to be greater in patients with calcific deposits than in those without. It has been suggested that the pattern of distribution of coronary calcific deposits within coronary arteries is of greater predictive value for acute coronary events than the overall quantity. Whether roughness of calcific deposits is a predictor of acute coronary events is not known. We derived and tested an algorithm, Voxel-Based Bosselation (VBB), for noninvasive quantification of roughness of calcific deposits in human coronary arteries imaged by computed tomography (CT). VBB was tested on 213 coronary calcific deposits from electron beam CT scans of 27 patients. This algorithm evaluates the 3-dimensional connectedness of surface voxels of each deposit: smooth masses have low VBB and rough masses high VBB. The algorithm was calibrated with artificially generated phantoms as well as background noise mimicking calcific deposits and surrounding heart tissue. The VBB algorithm is applicable to calcific deposits of all scales and gradations. The VBB values of the deposits in this study did not correlate with deposit size further supporting its validity as a measurement of roughness. The VBB index corresponded directly with visual reconstruction using Phong-shaded algorithms. The VBB index, derived here, is a noninvasive method of quantifying the roughness of calcific deposits in CT scan data which can now be used in future clinical studies to determine possible correlations with increased plaque vulnerability and major acute coronary events.