Construction of a Coronary Artery Atlas from CT Angiography

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


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.


Coronary Compute Tomographic Angiography Major Adverse Cardiac Event Stent Design Bifurcation Angle Coronary Artery Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 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
  2. 2.
    Dryden, I.L., Mardia, K.V.: Statistical shape analysis, vol. 4. John Wiley & Sons, New York (1998)zbMATHGoogle Scholar
  3. 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
  4. 4.
    Feragen, A., Lo, P., de Bruijne, M., Nielsen, M., Lauze, F.: Towards a theory of statistical tree-shape analysis. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 2008–2021 (2013)CrossRefGoogle Scholar
  5. 5.
    Fiss, D.M.: Normal coronary anatomy and anatomic variations. Appl. Radiol. 36(1), 14–26 (2007)Google Scholar
  6. 6.
    Go, A.S., Mozaffarian, D., Roger, V.L., Benjamin, E.J., Berry, J.D., Borden, W.B., Bravata, D.M., Dai, S., Ford, E.S., Fox, C.S., et al.: Heart disease and stroke statistics–2013 update: a report from the american heart association. Circulation 127(1), e6 (2013)CrossRefGoogle Scholar
  7. 7.
    Greenland, P., Bonow, R.O.: How low-risk is a coronary calcium score of zero? The importance of conditional probability. Circulation 117(13), 1627–1629 (2008)CrossRefGoogle Scholar
  8. 8.
    Loukas, M., Groat, C., Khangura, R., Owens, D.G., Anderson, R.H.: The normal and abnormal anatomy of the coronary arteries. Clin. Anat. 22(1), 114–128 (2009)CrossRefGoogle Scholar
  9. 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
  10. 10.
    Pflederer, T., Ludwig, J., Ropers, D., Daniel, W.G., Achenbach, S.: Measurement of coronary artery bifurcation angles by multidetector computed tomography. Invest. Radiol. 41(11), 793–798 (2006)CrossRefGoogle Scholar
  11. 11.
    Rosset, A., Spadola, L., Ratib, O.: Osirix: an open-source software for navigating in multidimensional dicom images. J. Digit. Imaging 17(3), 205–216 (2004)CrossRefGoogle Scholar
  12. 12.
    Rubinshtein, R., Lerman, A., Spoon, D.B., Rihal, C.S.: Anatomic features of the left main coronary artery and factors associated with its bifurcation angle: A 3-dimensional quantitative coronary angiographic study. Catheter. Cardiovasc. Interv. 80(2), 304–309 (2012)CrossRefGoogle Scholar
  13. 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
  14. 14.
    Wang, C., Smedby, Ö.: Coronary artery segmentation and skeletonization based on competing fuzzy connectedness tree. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 311–318. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 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)CrossRefzbMATHGoogle Scholar
  16. 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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dept. Anatomy with RadiologyUniversity of AucklandNew Zealand
  2. 2.Auckland Heart GroupAucklandNew Zealand
  3. 3.Auckland City HospitalAucklandNew Zealand
  4. 4.Center for Medical Image Science and Vis.Linköping University HospitalSweden

Personalised recommendations