Coronary extraction and characterization in multi-detector computed tomography

Abstract

The last technological advances of multi-detector computed tomography (MDCT) have brought significant improvements in image quality and consequently diagnostic performance. It opens, in particular, new perspectives for the coronary artery disease assessment. This paper presents a method for the coronary artery extraction and the lesion quantification. It makes use of a two stage-process. A tracking algorithm of the vascular structure, based on a 3D geometric moment operator, is first applied to estimate the vessel central axis location and its local diameter. It led to the extraction of the overall coronary tree. A deformable model technique, using a modified version of 3D level set technique, is then run on pathological vascular segments to refine the delineation of the wall and parietal calcifications. Parameters such as the narrowing or enlargement ratio of the vascular lumen, the minimum/maximum areas and the percent-area stenosis or aneurysm, are then computed for diagnosis purpose. The first method was submitted to a clinical evaluation on six data sets that showed good sensitivity and specificity performances. The second one was validated by experts, through a visual inspection of 2D cross-sections and volume rendering.