A Three-Dimensional Quantification of Calcified and Non-calcified Plaque Based on Computed Tomography Coronary Angiography Images: Comparison with Virtual Histology Intravascular Ultrasound

Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 68/1)


The identification, quantification and characterization of coronary atherosclerotic plaque has a major influence on diagnosis and treatment of coronary artery disease (CAD). Recent studies have reported the ability of Computed Tomography Coronary Angiography (CTCA) to identify non-invasively coronary plaque features. In this study, we present a novel methodology for the identification of the plaque burden of the coronary artery and the volumetric quantification of calcified plaques (CP) and non-calcified plaques (NCP), utilizing CTCA images in comparison with virtual histology intravascular ultrasound (VH-IVUS). The proposed methodology includes seven steps: CTCA images pre-processing, blooming effect removal, vessel centerline extraction using Multistencil Fast Marching Method (MSFM), estimation of membership sigmoidal distribution functions, implementation of an extension of active contour models using prior shapes for the lumen, the outer wall and CP segmentation, detection and quantification of NCP and finally three-dimensional (3D) models construction. Bland Altman and correlation plot analyses were performed to assess the agreement between the presented methodology and VH-IVUS. Assessment of volume and length of lesion length in 18 lesions indicated good correlation with VH-IVUS. More specifically, the Pearson’s correlation (r) is (r = 0.93, p < 0.001) and (r = 0.92, p < 0.001) for CP and NCP volume, respectively, while the correlation for the length of lesion is (r = 0.84, p < 0.001) and (r = 0.95, p < 0.001) for CP and NCP, respectively.


Computed tomography angiography Coronary artery disease Coronary arteries Atherosclerotic plaque Calcified plaque Non-calcified plaque Level-set segmentation 



This work is funded by the European Commission: Project SMARTOOL, “Simulation Modeling of coronary ARTery disease: a tool for clinical decision support—SMARTool” GA number: 689068).


  1. 1.
    Voros, S., et al., Coronary Atherosclerosis Imaging by Coronary CT Angiography: Current Status, Correlation With Intravascular Interrogation and Meta-Analysis. JACC: Cardiovascular Imaging, 2011. 4(5): p. 537–548.Google Scholar
  2. 2.
    Dey, D., et al., Automated Three-dimensional Quantification of Noncalcified Coronary Plaque from Coronary CT Angiography: Comparison with Intravascular US. Radiology, 2010. 257(2): p. 516–522.Google Scholar
  3. 3.
    Brodoefel, H., et al., Accuracy of dual-source CT in the characterisation of non-calcified plaque: use of a colour-coded analysis compared with virtual histology intravascular ultrasound. The British Journal of Radiology, 2009. 82(982): p. 805–812.Google Scholar
  4. 4.
    de Graaf, M.A., et al., Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology. The International Journal of Cardiovascular Imaging, 2013. 29(5): p. 1177–1190.Google Scholar
  5. 5.
    Jawaid, M., et al., Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles. Vol. 89. 2017.Google Scholar
  6. 6.
    Frangi, A.F., et al., Multiscale vessel enhancement filtering, in Medical Image Computing and Computer-Assisted Intervention — MICCAI’98: First International Conference Cambridge, MA, USA, October 11–13, 1998 Proceedings, W.M. Wells, A. Colchester, and S. Delp, Editors. 1998, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 130–137.Google Scholar
  7. 7.
    Castillo-Amor, A.M., et al. Reduction of blooming artifacts in cardiac CT images by blind deconvolution and anisotropic diffusion filtering, in 11th International Symposium on Medical Information Processing and Analysis. 2015. International Society for Optics and Photonics.Google Scholar
  8. 8.
    Metz, C., et al., Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach. Vol. 36. 2009. 5568–79.Google Scholar
  9. 9.
    Chan, T.F. and L.A. Vese, Active contours without edges. IEEE Transactions on Image Processing, 2001. 10(2): p. 266–277.Google Scholar
  10. 10.
    Chan, T. and Z. Wei. Level set based shape prior segmentation, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). 2005.Google Scholar
  11. 11.
    Cremers, D., N. Sochen, and C. Schnörr, Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling, in Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings, L.D. Griffin and M. Lillholm, Editors. 2003, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 388–400.Google Scholar
  12. 12.
    Dalager, M.G., et al., Impact of luminal density on plaque classification by CT coronary angiography. The International Journal of Cardiovascular Imaging, 2011. 27(4): p. 593–600.Google Scholar
  13. 13.
    Lorensen, W.E. and H.E. Cline, Marching cubes: A high resolution 3D surface construction algorithm, in Proceedings of the 14th annual conference on Computer graphics and interactive techniques. 1987, ACM. p. 163–169.Google Scholar
  14. 14.
    V Bourantas, C., et al., A new methodology for accurate 3-dimensional coronary artery reconstruction using routine intravascular ultrasound and angiographic data: Implications for widespread assessment of endothelial shear stress in humans. Vol. 9. 2013.Google Scholar
  15. 15.
    Medina, R., et al., Three methods for accurate quantification of plaque volume in coronary arteries. The International Journal of Cardiovascular Imaging, 2003. 19(4): p. 301–311.Google Scholar
  16. 16.
    Kigka, V.I., et al., 3D reconstruction of coronary arteries and atherosclerotic plaques based on computed tomography angiography images. Biomedical Signal Processing and Control, 2018. 40(Supplement C): p. 286–294.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of IoanninaIoanninaGreece
  2. 2.Department of Biomedical Research Institute – FORTHInstitute of Molecular Biology and Biotechnology, University Campus of IoanninaIoanninaGreece
  3. 3.Department of Cardiology, Medical SchoolUniversity of IoanninaIoanninaGreece

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