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Proceedings Paper

Coronary artery remodeling in non-contrast CT images
Author(s): Haiyong Xu; Mingna Zheng; Yanhua Yang; J. Jeffery Carr; Yaorong Ge
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Paper Abstract

A significant cause of coronary artery disease is the coronary atherosclerosis which leads to stenosis of coronary arteries. It has been shown in recent studies, using intravascular ultrasound and contrast-enhanced CT, that early atherosclerosis causes positive coronary artery remodeling, defined as increases in the cross-sectional area. It is hypothesized that detection of artery remodeling using non-contrast CT can be an important factor in sub-clinical assessment of cardiac risk for asymptomatic subjects. However, measuring remodeling in coronary arteries in non-contrast CT images is a challenging task because coronary arteries are small and the intensity of coronary arteries is similar to that of surrounding tissues. Automatic segmentation algorithms that have been successful in segmenting coronary arteries in contrast-enhanced images do not perform well. To overcome these difficulties, we developed an interactive application to enable effective measurement of coronary artery remodeling in non-contrast CT images. This application is an extension to the 3D Slicer image analysis platform. It allows users to visualize and trace the centerline of arteries in cross sectional views. The artery centerlines are displayed in a three dimensional view overlaid on the original image volume and color-coded according to the artery labels. Using this 3D artery model, the user can sample the cross-sectional area of the arteries at selected points for remodeling assessment. Initial validation has demonstrated the effectiveness of this method. A pilot study also showed positive correlation of large coronary artery remodeling with highest lifetime risks. Further evaluation is underway using larger study size and more measurement points.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151F (23 February 2012); doi: 10.1117/12.911730
Show Author Affiliations
Haiyong Xu, Wake Forest Univ. School of Medicine (United States)
Mingna Zheng, Wake Forest Univ. School of Medicine (United States)
Yanhua Yang, Wake Forest Univ. School of Medicine (United States)
J. Jeffery Carr, Wake Forest Univ. School of Medicine (United States)
Yaorong Ge, Wake Forest Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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