Journal of the Korean Physical Society

, Volume 66, Issue 4, pp 558–570 | Cite as

Automated measurement of stent strut coverage in intravascular optical coherence tomography

  • Chi Young Ahn
  • Byeong-Keuk Kim
  • Myeong-Ki Hong
  • Yangsoo Jang
  • Jung Heo
  • Chulmin Joo
  • Jin Keun Seo
Article

Abstract

Optical coherence tomography (OCT) is a non-invasive, cross-sectional imaging modality that has become a prominent imaging method in percutaneous intracoronary intervention. We present an automated detection algorithm for stent strut coordinates and coverage in OCT images. The algorithm for stent strut detection is composed of a coordinate transformation from the polar to the Cartesian domains and application of second derivative operators in the radial and the circumferential directions. Local region-based active contouring was employed to detect lumen boundaries. We applied the method to the OCT pullback images acquired from human patients in vivo to quantitatively measure stent strut coverage. The validation studies against manual expert assessments demonstrated high Pearson’s coefficients (R = 0.99) in terms of the stent strut coordinates, with no significant bias. An averaged Hausdorff distance of < 120 μm was obtained for vessel border detection. Quantitative comparison in stent strut to vessel wall distance found a bias of < 12.3 μm and a 95% confidence of < 110 μm.

Keywords

Optical coherence tomography Stent struts Automatic measurement Second derivative 

References

  1. [1]
    G. K. Hansson, N. Engl. J. Med. 352, 1685 (2005).CrossRefGoogle Scholar
  2. [2]
    P. A. Lemos, F. Saia, J. M. Ligthart, C. A. Arampatzis, G. Sianos, K. Tanabe, A. Hoye, M. Degertekin, J. Daemen, et al., Circulation 108, 257 (2003).CrossRefGoogle Scholar
  3. [3]
    D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, et al., Science 254, 1178 (1991).CrossRefADSGoogle Scholar
  4. [4]
    P. Naithani, R. Sihota, P. Sony, T. Dada, V. Gupta, D. Kondal and R. M. Pandey, Invest. Ophthalmol. Vis. Sci. 48, 3138 (2007).CrossRefGoogle Scholar
  5. [5]
    M. V. Jr. Sivak, K. Kobayashi, J. A. Izatt, A. M. Rollins, R. Ung-Runyawee, A. Chak, R. C. Wong, G. A. Isenberg and J. Willis, Gastrointest Endosc. 51, 474 (2000).CrossRefGoogle Scholar
  6. [6]
    I. K. Jang, B. E. Bouma, D. H. Kang, S. J. Park, S. W. Park, K. B. Seung, K. B. Choi, M. Shishkov, K. Schlendorf, et al., J. Am. Coll. Cardiol. 39, 604 (2002).CrossRefGoogle Scholar
  7. [7]
    H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins and D. I. Simon, JACC Cardiovasc. Interv. 2, 1035 (2009).CrossRefGoogle Scholar
  8. [8]
    G. Guagliumi and V. Sirbu, Catheter Cardiovasc. Interv. 72, 237 (2008).CrossRefGoogle Scholar
  9. [9]
    G. Unal, S. Gurmeric and S. G. Carlier, Int. J. Cardiovasc. Imaging 26, 809 (2010).CrossRefGoogle Scholar
  10. [10]
    C. Kauffmann, P. Motreff and L. Sarry, IEEE Trans. Med. Imaging 29, 807 (2010).CrossRefGoogle Scholar
  11. [11]
    G. J. Ughi, T. Adriaenssens, K. Onsea, P. Kayaert, C. Dubois, P. Sinnaeve, M. Coosemans, W. Desmet and J. D’hooge, Int. J. Cardiovasc. Imaging 28, 229 (2012).CrossRefGoogle Scholar
  12. [12]
    K. P. Tung, W. Z. Shi, L. Pizarro, H. Tsujioka, H.-Y. Wang, R. Guerrero, R. de Silva, P. E. Edwards and D. Rueckert, Proc. SPIE, 8315, 83150K (2012).CrossRefGoogle Scholar
  13. [13]
    C. Xu, J. M. Schmitt, T. Akasaka, T. Kubo and K. Huang, Phys. Med. Biol. 56, 6665 (2011).CrossRefGoogle Scholar
  14. [14]
    S. Gurmeric, G. G. Isguder, S. Carlier and G. Unal, Med. Image Comput. Assist. Interv. 12, 776 (2009).Google Scholar
  15. [15]
    N. Bruining, K. Sihan, J. Ligthart, P. Cummins, S. De Winter and E. Regar, Proc. of Comput. Cardiol. 58, 221 (2011).Google Scholar
  16. [16]
    S. Tsantis, G. C. Kagadis, K. Katsanos, D. Karnabatidis, G. Bourantas and G. C. Nikiforidis, Med. Phys. 39, 503 (2012).CrossRefGoogle Scholar
  17. [17]
    C. Kauffmann, B. Godbout and J. A. de Guise, Proc. SPIE 3338, 663 (1998).CrossRefADSGoogle Scholar
  18. [18]
    V. Caselles, F. Catte, T. Coll and F. Dibos, Numer. Math. 66, 1 (1993).CrossRefMATHMathSciNetGoogle Scholar
  19. [19]
    J. Sethian, Level set methods and fast marching methods, 2 nd edition (Cambridge University Press, Cambridge, 1999).MATHGoogle Scholar
  20. [20]
    S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces (Springer-Verlag, New York, 2003).CrossRefMATHGoogle Scholar
  21. [21]
    J. L. Barron, D. J. Fleet and S. S. Beauchemin, Int. J. Comput. Vision 12, 43 (1994).CrossRefGoogle Scholar
  22. [22]
    D. P. Huttenlocher, G. A. Klanderman and W. J. Rucklidge, IEEE Trans. Pattern Anal. Machine Intell. 15, 850 (1993).CrossRefGoogle Scholar
  23. [23]
    E. Belogay, C. Cabrelli, U. Molter and R. Shonkwiler, Inform. Process. Lett. 64, 17 (1997).CrossRefMathSciNetGoogle Scholar
  24. [24]
    V. E. Pera, E. L. Heffer, H. Siebold, O. Schutz, S. Heywang-Kobrunner, L. Gotz, A. Heinig and S. Fantini, J. Biomed. Opt. 8, 517 (2003).CrossRefADSGoogle Scholar
  25. [25]
    S. Lankton and A. Tannenbaum, IEEE Trans. Image Proc. 17, 2029 (2008).CrossRefADSMathSciNetGoogle Scholar
  26. [26]
    G. T. Bonnema, K. O. Cardinal, S. K. Williams and J. K. Barton, Phys. Med. Biol. 53, 3083 (2008).CrossRefGoogle Scholar
  27. [27]
    A. Wang, J. Eggermont, N. Dekker, H. M. Garcia-Garcia, R. Pawar, J. H. Reiber and J. Dijkstra, Int. J. Cardiovasc. Imaging 29, 29 (2013).CrossRefGoogle Scholar

Copyright information

© The Korean Physical Society 2015

Authors and Affiliations

  • Chi Young Ahn
    • 1
  • Byeong-Keuk Kim
    • 2
  • Myeong-Ki Hong
    • 2
  • Yangsoo Jang
    • 2
  • Jung Heo
    • 3
  • Chulmin Joo
    • 3
  • Jin Keun Seo
    • 4
  1. 1.National Institute for Mathematical SciencesDaejeonKorea
  2. 2.Division of CardiologySeverance Cardiovascular HospitalSeoulKorea
  3. 3.School of Mechanical EngineeringYonsei UniversitySeoulKorea
  4. 4.Department of Computational Science and EngineeringYonsei UniversitySeoulKorea

Personalised recommendations