Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Validation of right coronary artery lumen area from cardiac computed tomography against intravascular ultrasound

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Quantification of coronary artery disease (CAD) from cardiac computed tomography angiography (CTA) is important both structurally (lumen area stenosis) and functionally (combined with computational fluid dynamics to determine fractional flow reserve) for assessment of ischemic stenosis and to guide treatment. Hence, it is important to have CTA image processing technique for segmentation and reconstruction of coronary arteries. In this study, we developed segmentation and reconstruction techniques, based on fast marching and Runge–Kutta methods for centerline extraction, and surface mesh generation. The accuracy of the reconstructed models was validated with direct intravascular ultrasound (IVUS) measurements in 1950 cross sections within 4 arteries. High correlation was found between CTA and IVUS measurements for lumen areas (\(r=0.993\), \(p<0.001\)). Receiver-operating characteristic (ROC) curves showed excellent accuracies for detection of different cutoff values of cross-lumen area (5 \(\text {mm}^2\), 6 \(\text {mm}^2\), 7 \(\text {mm}^2\) and 8 \(\text {mm}^2\), all ROC values >0.99). We conclude that our technique has sufficient accuracy for quantifying coronary lumen area. The accuracy and efficiency demonstrated that our approach can facilitate quantitative evaluation of coronary stenosis and potentially help in real-time assessment of CAD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kirişli, H.A., Schaap, M., Metz, C.T., Dharampal, A.S., Meijboom, W.B., Papadopoulou, S.L., et al.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 17(8), 859–876 (2013)

    Article  Google Scholar 

  2. Caussin, C., Larchez, C., Ghostine, S., Pesenti-Rossi, D., Daoud, B., Habis, M., et al.: Comparison of coronary minimal lumen area quantification by sixty-four-slice computed tomography versus intravascular ultrasound for intermediate stenosis. Am. J. Cardiol. 98(7), 871–876 (2006)

    Article  Google Scholar 

  3. Nieman, K., Cademartiri, F., Lemos, P.A., Raaijmakers, R., Pattynama, P.M., de Feyter, P.J.: Reliable noninvasive coronary angiography with fast submillimeter multislice spiral computed tomography. Circulation 106, 2051–2054 (2002)

    Article  Google Scholar 

  4. Samuels, O.B., Joseph, G.J., Lynn, M.J., Smith, H.A., Chimowitz, M.I.: A standardized method for measuring intracranial arterial stenosis. Am. J. Neuroradiol. 21, 643–646 (2000)

    Google Scholar 

  5. Zhang, J.M., Zhong, L., Su, B., Wan, M., Yap, J.S., Tham, J.P.: Perspective on cfd studies of coronary artery disease lesions and hemodynamics: a review. Int. J. Numer. Method Biomed. Eng. 30, 659–680 (2014)

    Article  MathSciNet  Google Scholar 

  6. Arbab-Zadeh, A., Hoe, J.: Quantification of coronary arterial stenoses by multidetector ct angiography in comparison with conventional angiography: methods, caveats, and implications. JACC. Cardiovasc. Imaging 4, 191–202 (2011)

    Article  Google Scholar 

  7. Nalcioglu, O., Roeck, W.W., Reese, T., Qu, L.Z., Tobis, J.M., Henry, W.L.: Background subtraction algorithms for videodensitometric quantification of coronary stenosis. Mach. Vis. Appl. 1(3), 155–162 (1988)

    Article  Google Scholar 

  8. Topol, E.J., Nissen, S.E.: Our preoccupation with coronary luminology the dissociation between clinical and angiographic findings in ischemic heart disease. Circulation 92, 2333–2342 (1995)

    Article  Google Scholar 

  9. Kruk, M., Wardziak, Ł., Mintz, G.S., Achenbach, S., Pręgowski, J., Rużyłło, W., et al.: Accuracy of coronary computed tomography angiography vs intravascular ultrasound for evaluation of vessel area. J. Cardiovasc. Comput. Tomogr. 8, 141–148 (2014)

    Article  Google Scholar 

  10. Marquering, H.A., Dijkstra, J., de Koning, P.J., Stoel, B.C., Reiber, J.H.: Towards quantitative analysis of coronary cta. Int. J. Cardiovasc. Imaging 21, 73–84 (2005)

    Article  Google Scholar 

  11. Luo, T., Wischgoll, T., Kwon Koo, B., Huo, Y., Kassab, G.S., Secomb, T.W.: IVUS validation of patient coronary artery lumen area obtained from ct images. PloS one 9, e86949 (2014)

    Article  Google Scholar 

  12. Behrens, T., Rohr, K., Stiehl, H.S.: Robust segmentation of tubular structures in 3-d medical images by parametric object detection and tracking. IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 33(4), 554–561 (2003)

    Article  Google Scholar 

  13. Angelova, D., Mihaylova, L.: Contour segmentation in 2D ultrasound medical images with particle filtering. Mach. Vis. Appl. 22(3), 551–561 (2011)

    Google Scholar 

  14. Zhang, L., Peng, X., Li, G., Li, H.: A novel active contour model for image segmentation using local and global region-based information. Mach. Vis. Appl. 28(1–2), 75–89 (2017)

    Article  Google Scholar 

  15. Tian, Y., Duan, F., Zhou, M., Wu, Z.: Active contour model combining region and edge information. Mach. Vis. Appl. 24(1), 1–15 (2013)

    Article  Google Scholar 

  16. Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model-based detection of tubular structures in 3d images. Comput. Vis. Image Und. 80(2), 130–171 (2000)

    Article  Google Scholar 

  17. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI’98, pp. 130–137. Springer, Berlin (1998)

  18. Almasi, D., Ghandchi, M.: Autom. Vessel Segm. Based Reg. Grow. 2(2):899–902 (2015)

  19. Cornea, N.D., Silver, D., Min, P.: Curve-skeleton properties, applications, and algorithms. IEEE Trans. Vis. Comput. Graph 3, 530–548 (2007)

    Article  Google Scholar 

  20. Schaap, M., Metz, C.T., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 13(5), 701–714 (2009)

    Article  Google Scholar 

  21. Yang, G., Kitslaar, P., Frenay, M., Broersen, A., Boogers, M.J., Bax, J.J., et al.: Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int. J. Cardiovasc. Imaging 28(4), 921–933 (2012)

    Article  Google Scholar 

  22. Metz, C.T.: Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach. Med. Phys. 36(12), 5568–5579 (2009)

    Article  Google Scholar 

  23. Van Uitert, R., Bitter, I.: Subvoxel precise skeletons of volumetric data based on fast marching methods. Med. Phys. 34, 627–638 (2007)

    Article  Google Scholar 

  24. Frangi, A.F., Niessen, W.J., Hoogeveen, R.M., Walsum, T.V., Viergever, M.A.: Model-based quantitation of 3-d magnetic resonance angiographic images. IEEE Trans. Med. Imaging 18(10), 946–956 (1999)

    Article  Google Scholar 

  25. Weickert, J.: Coherence-enhancing diffusion filtering. Int. J. Comput. Vis. 31(2–3), 111–127 (1999)

    Article  Google Scholar 

  26. Weickert, J.: Anisotropic Diffusion in Image Processing, vol. 1. Teubner, Stuttgart (1998)

    MATH  Google Scholar 

  27. Tasdizen, T., Whitaker, R., Marc, R., Jones, B.: Enhancement of cell boundaries in transmission electron microscopy images. IEEE Int. Conf. Image Process. 2, 129–132 (2005)

    Google Scholar 

  28. Manniesing, R., Niessen, W.: Multiscale vessel enhancing diffusion in ct angiography noise filtering. In: Information Processing in Medical Imaging, pp. 138–149. Springer, Berlin (2005)

  29. Manniesing, R., Viergever, M.A., Niessen, W.J.: Vessel enhancing diffusion: a scale space representation of vessel structures. Med. Image Anal. 10(6), 815–825 (2006)

    Article  Google Scholar 

  30. Cui, H., Wang, D., Min, W., Zhang, J.M., Zhao, X., Ru, S.T., et al.: Coronary artery segmentation via hessian filter and curve-skeleton extraction. In: 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 93-98 (2014)

  31. Cui, H., Wang, D., Min, W., Zhang, J.M., Zhao, X., Ru, S.T.: Fast marching and Runge-Kutta based method for centreline extraction of right coronary artery in human patients. Cardiovasc. Eng. Technol. 7(2), 159 (2016)

    Article  Google Scholar 

  32. Cai, W., Harris, G.J., Yoshida, H.: Computation of vesselness in CTA images for fast and interactive vessel segmentation. Int. J. Image Gr. 7, 159–176 (2007)

    Article  Google Scholar 

  33. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  34. Süli, E., Mayers, D.F.: An Introduction to Numerical Analysis. Cambridge University Press, Cambridge (2003)

    Book  Google Scholar 

  35. Canero, C., Radeva, P.: Vesselness enhancement diffusion. Pattern Recognit. Lett. 24(16), 3141–3151 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61471297, 61771397 and 61801393, in part by the China Postdoctoral Science Foundation under Grant 2017M623245, in part by the Fundamental Research Funds for the Central Universities under Grant 3102018zy031, and in part by the National Medical Research Council (NMRC/BnB/1007/2015).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Xia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, H., Xia, Y., Zhang, Y. et al. Validation of right coronary artery lumen area from cardiac computed tomography against intravascular ultrasound. Machine Vision and Applications 29, 1287–1298 (2018). https://doi.org/10.1007/s00138-018-0978-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0978-z

Keywords

Navigation