Evaluation of computer-assisted quantification of carotid artery stenosis

Abstract

The purpose of this study was to evaluate the influence of advanced software assistance on the assessment of carotid artery stenosis; particularly, the inter-observer variability of readers with different level of experience is to be investigated. Forty patients with suspected carotid artery stenosis received head and neck dual-energy CT angiography as part of their pre-interventional workup. Four blinded readers with different levels of experience performed standard imaging interpretation. At least 1 day later, they performed quantification using an advanced vessel analysis software including automatic dual-energy bone and hard plaque removal, automatic and semiautomatic vessel segmentation, as well as creation of curved planar reformation. Results were evaluated for the reproducibility of stenosis quantification of different readers by calculating the kappa and correlation values. Consensus reading of the two most experienced readers was used as the standard of reference. For standard imaging interpretation, experienced readers reached very good (k = 0.85) and good (k = 0.78) inter-observer variability. Inexperienced readers achieved moderate (k = 0.6) and fair (k = 0.24) results. Sensitivity values 80%, 91%, 83%, 77% and specificity values 100%, 84%, 82%, 53% were achieved for significant area stenosis >70%. For grading using advanced vessel analysis software, all readers achieved good inter-observer variability (k = 0.77, 0.72, 0.71, and 0.77). Specificity values of 97%, 95%, 95%, 93% and sensitivity values of 84%, 78%, 86%, 92% were achieved. In conclusion, when supported by advanced vessel analysis software, experienced readers are able to achieve good reproducibility. Even inexperienced readers are able to achieve good results in the assessment of carotid artery stenosis when using advanced vessel analysis software.

Fig. 1

Flowchart shows steps performed for the preparation of data for standard image interpretation and image interpretation using advanced vessel analysis software

Fig. 2

Graphical user interface right after loading of case study. Carotid arteries of both sides are segmented and two arteries are visualized in CPR view. Selection of other carotid artery is possible via selection of labels in VRT. Measurement of stenotic lesions can be performed on CPR view

Fig. 3

Measurement of a stenotic lesion. Positioning of markers is initialized via a single user click. Position of the narrowest lumen and the reference position are determined based on the area profile curve. To set the stenosis marker, the local minimum is defined around click point. Reference position is set on start of closest distal plateau

Fig. 4

Examples for results of contouring algorithm in presence of calcified plaque, stent material, and in the absence of plaque. Windowing were set for screenshots according to the rule of Saba et.al. [29]. Window settings have no influence on the contouring algorithm