Automated quantification of stenosis severity on 64-slice CT: a comparison with quantitative coronary angiography.

OBJECTIVES: This study sought to demonstrate the feasibility of a dedicated algorithm for automated quantification of stenosis severity on multislice computed tomography in comparison with quantitative coronary angiography (QCA). BACKGROUND: Limited information is available on quantification of coronary stenosis, and previous attempts using semiautomated approaches have been suboptimal. METHODS: In patients who had undergone 64-slice computed tomography and invasive coronary angiography, the most severe lesion on QCA was quantified per coronary artery using quantitative coronary computed tomography (QCCTA) software. Additionally, visual grading of stenosis severity using a binary approach (50% stenosis as a cutoff) was performed. Diameter stenosis (percentage) was obtained from detected lumen contours at the minimal lumen area, and corresponding reference diameter values were obtained from an automatic trend analysis of the vessel areas within the artery. RESULTS: One hundred patients (53 men; 59.8 +/- 8.0 years) were evaluated, and 282 (94%) vessels were analyzed. Good correlations for diameter stenosis were observed for vessel-based (n = 282; r = 0.83; p < 0.01) and patient-based (n = 93; r = 0.86; p < 0.01) analyses. Mean differences between QCCTA and QCA were -3.0% +/- 12.3% and -6.2% +/- 12.4%. Furthermore, good agreement was observed between QCCTA and QCA for semiquantitative assessment of diameter stenosis (accuracy of 95%). Diagnostic accuracy for assessment of > or =50% diameter stenosis was higher using QCCTA compared with visual analysis (95% vs. 87%; p = 0.08). Moreover, a significantly higher positive predictive value was observed with QCCTA when compared with visual analysis (100% vs. 78%; p < 0.05). Although the visual approach showed a reduced diagnostic accuracy for data sets with moderate image quality, QCCTA performed equally well in patients with moderate or good image quality. However, in data sets with good image quality, QCCTA tended to have a reduced sensitivity compared with visual analysis. CONCLUSIONS: Good correlations were found for quantification of stenosis severity between QCCTA and QCA. QCCTA showed an improved positive predictive value when compared with visual analysis.

Automatic vessel wall contour detection and quantification of wall thickness in in-vivo MR images of the human aorta.

PURPOSE: To develop an automated technique to trace the contours of the lumen and outer boundary of the aortic wall, and measure aortic wall thickness in axial MR images. MATERIALS AND METHODS: The algorithm uses prior knowledge of vessel wall morphology. A geometrical model (ellipse) is deformed, translated and rotated to obtain a rough approximation of the contours. Model-matching is based on image gradient measurements. To enhance edges, the images were preprocessed using gray-level stretching. Refinement is performed by means of dynamic programming. Wall thickness is computed by measuring the distance between inner and outer contour of the aortic wall. RESULTS: The algorithm has been tested on high-resolution axial MR images from 28 human subjects of the descending thoracic aorta. The results demonstrate: High correspondence between automatic and manual area measurements: lumen (r = 0.99), outer (r = 0.96), and wall thickness (r = 0.85). CONCLUSION: Though further optimization is required, our algorithm is a powerful tool to automatically draw the boundaries of the aortic wall and measure aortic wall thickness in aortic wall devoid of major lesions. J. Magn. Reson. Imaging 2006. (c) 2006 Wiley-Liss, Inc.

An integrated automated analysis method for quantifying vessel stenosis and plaque burden from carotid MRI images: combined postprocessing of MRA and vessel wall MR.

BACKGROUND AND PURPOSE: We report the evaluation of a semiautomated method for in vivo assessment of the severity of carotid atherosclerosis with minimal user interaction that combines 3-dimensional contrast-enhanced magnetic resonance angiography (CE-MRA) and vessel wall magnetic resonance imaging (MRI). METHODS: Lumen and outer-wall contours were automatically detected, and stenosis and plaque burden were estimated. The method was tested on 22 subjects (352 postcontrast, T1-weighted cross sections and 3-dimensional CE-MRA). RESULTS: We observed good correlation with expert contours: lumen and outer-wall area (r=0.96) and the degree of stenosis (r=0.97). CONCLUSIONS: The fusion of MRA and MRI reduces user interaction and improves contour detection, providing reproducible parameters to assess the severity of atherosclerosis.