Automated quantification of carotid artery stenosis on contrast-enhanced MRA data using a deformable vascular tube
Avan Suinesiaputra1, Patrick J H de Koning, Elena Zudilova-Seinstra
1Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.
Abstract:
The purpose of this study was to develop and validate a method for automated segmentation of the carotid artery lumen from volumetric MR Angiographic (MRA) images using a deformable tubular 3D Non-Uniform Rational B-Splines (NURBS) model. A flexible 3D tubular NURBS model was designed to delineate the carotid arterial lumen. User interaction was allowed to guide the model by placement of forbidden areas. Contrast-enhanced MRA (CE-MRA) from 21 patients with carotid atherosclerotic disease were included in this study. The validation was performed against expert drawn contours on multi-planar reformatted image slices perpendicular to the artery. Excellent linear correlations were found on cross-sectional area measurement (r = 0.98, P < 0.05) and on luminal diameter (r = 0.98, P < 0.05). Strong match in terms of the Dice similarity indices were achieved: 0.95 ± 0.02 (common carotid artery), 0.90 ± 0.07 (internal carotid artery), 0.87 ± 0.07 (external carotid artery), 0.88 ± 0.09 (carotid bifurcation) and 0.75 ± 0.20 (stenosed segments). Slight overestimation of stenosis grading by the automated method was observed. The mean differences was 7.20% (SD = 21.00%) and 5.2% (SD = 21.96%) when validated against two observers. Reproducibility in stenosis grade calculation by the automated method was high; the mean difference between two repeated analyses was 1.9 ± 7.3%. In conclusion, the automated method shows high potential for clinical application in the analysis of CE-MRA of carotid arteries.
