Quantitative measurements of coronary arterial stenosis, plaque geometry, and composition are highly reproducible with a standardized coronary arterial computed tomographic approach in high-quality CT datasets.

Background: Computed tomographic (CT) coronary angiography provides a noninvasive method for coronary plaque detection and quantification, but data are limited on reproducibility of a quantitative evaluation.

Methods: Intrarater and interrater reliability of a semiquantitative and highly standardized, fully quantitative approach was evaluated in 480 coronary segments in 30 patients. Quantitative vessel-wall and plaque geometrical parameters (minimal lumen diameter [MLD], minimal lumen area [MLA], percentage of atheroma volume [PAV], and remodeling index [RI]) and compositional parameters (calcified plaque volume [CAP] and % of CAP [%CAP], noncalcified plaque [NCP] and % of NCP [%NCP], high-density NCP volume [HD-NCP] and % of HD-NCP [%HD-NCP] and low-density NCP volume [LD-NCP] and % of LD-NCP [%LD-NCP]) were measured. Semiquantitative agreement was evaluated by weighted κ; quantitative agreement was evaluated by concordance correlation coefficient (CCC) and Bland-Altman analysis.

Results: Intraobserver agreement for MLD, MLA, and RI was excellent (CCC: 0.96, 0.96, and 0.84, respectively). Intraobserver agreement for %CAP, %HD-NCP, and %LD-NCP was also excellent (CCC: 0.99, 0.98,and 0.96, respectively). Interobserver agreement for MLD, MLA, PAV and RI was excellent (CCC: 0.98, 0.99, 0.96,and 0.86, respectively). Interobserver agreement for %CAP, % NCP, %HD-NCP, and %LD-NCP was also excellent (CCC: 0.99, 0.99, 0.98,and 0.90, respectively), and mean differences were small. Quantitative analysis showed statistically significant differences in both geometrical and compositional parameters between normal segments and those with plaque.

Conclusions: Standardized, quantitative analysis of coronary CTA datasets is reproducible for the measurement of plaque geometrical and compositional parameters and can quantify differences between normal and abnormal segments in high-quality datasets.