Automated quantification of carotid artery stenosis on contrast-enhanced MRA data using a deformable vascular tube model

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.

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline

“…Moreover, the balance between the constraint and the flexibility of the tube model is optimized in the training phase, which allows the algorithm to outline any regular and irregular vessel wall boundaries caused by stenosis or eccentric wall thickening. It is also worth noting that a previous study has demonstrated that the segmentation of stenotic carotid arteries in MRA images using a similar tube‐fitting method was in good agreement with the expert results in terms of lumen diameter, cross‐sectional area, and stenosis grading. Future studies need to be carried out to firmly establish the ability of the proposed algorithm to perform reliable segmentation of the vessel wall in patients with atherosclerotic plaque.…”

Patterns and Implications of Intracranial Arterial Remodeling in Stroke Patients

“…To accomplish this, the TOF-MRA was analyzed using LAVA software (LAVA, Leiden University Medical Center, the Netherlands), which uses a deformable tubular model based on Non-Uniform Rational B-Splines (NURBS) surface modeling to contour each vessel segment (Supplemental Figure II). This technique provides semi-automated contour detection of the arterial lumen 16 and performs an iterative linear regression fit of the lumen area over the entire segment. Vessel tapering, represented as the slope (S) of the regression line, was calculated as S = Δ area (mm 2 )/Δ distance (mm).…”

MR Imaging Measures of Intracranial Atherosclerosis in a Population-based Study

“…Vessel wall magnetic resonance (MR) imaging is an effective method with which to measure wall thickness and identify pathologic features of extracranial vessels (6 The lumen boundary was contoured on the MR angiogram by using nonuniform rational B-spline, or NURBS, surface modeling software (LAVA; Leiden University Medical Center, the Netherlands) (14) and coregistered with the black-blood MR image, which was used to generate outer wall contours also by means of NURBS modeling. Measurements of lumen size and stenosis were based on tubular model contours from MR angiography, and measurements of wall and/or plaque size were based on lumen and outer wall contours from a tubular model of the black-blood MR imaging segmentation (Fig 1).…”