Construction of a Coronary Artery Atlas from CT Angiography
Describing the detailed statistical anatomy of the coronary artery tree is important for determining the ætiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.
KeywordsCoronary Compute Tomographic Angiography Major Adverse Cardiac Event Stent Design Bifurcation Angle Coronary Artery Tree
Unable to display preview. Download preview PDF.
- 1.Daemen, J., Wenaweser, P., Tsuchida, K., Abrecht, L., Vaina, S., Morger, C., Kukreja, N., Jüni, P., Sianos, G., Hellige, G., et al.: Early and late coronary stent thrombosis of sirolimus-eluting and paclitaxel-eluting stents in routine clinical practice: data from a large two-institutional cohort study. The Lancet 369(9562), 667–678 (2007)CrossRefGoogle Scholar
- 3.Dzavik, V., Kharbanda, R., Ivanov, J., Douglas, J., Bui, S., Mackie, K., Ramsamujh, R., Barolet, A., Schwartz, L., Seidelin, P.H.: Predictors of long-term outcome after crush stenting of coronary bifurcation lesions: importance of the bifurcation angle. Am. Heart J. 152(4), 762–769 (2006)CrossRefGoogle Scholar
- 5.Fiss, D.M.: Normal coronary anatomy and anatomic variations. Appl. Radiol. 36(1), 14–26 (2007)Google Scholar
- 9.Lu, L., Bi, J., Yu, S., Peng, Z., Krishnan, A., Zhou, X.S.: Hierarchical learning for tubular structure parsing in medical imaging: A study on coronary arteries using 3D CT angiography. In: IEEE 12th Intl. Conf. on Computer Vision, pp. 2021–2028. IEEE (2009)Google Scholar
- 13.Wang, C., Frimmel, H., Smedby, Ö.: Level-set based vessel segmentation accelerated with periodic monotonic speed function. In: SPIE Medical Imaging, p. 79621M. International Society for Optics and Photonics (2011)Google Scholar
- 15.Williams, A.R., Koo, B.K., Gundert, T.J., Fitzgerald, P.J., LaDisa, J.F.: Local hemodynamic changes caused by main branch stent implantation and subsequent virtual side branch balloon angioplasty in a representative coronary bifurcation. J. Appl. Physiol. 109(2), 532–540 (2010)CrossRefMATHGoogle Scholar
- 16.Yang, G., Broersen, A., Petr, R., Kitslaar, P., de Graaf, M.A., Bax, J.J., Reiber, J., Dijkstra, J.: Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets. In: Computing in Cardiology, pp. 109–112. IEEE (2011)Google Scholar