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In Vivo OCT Coronary Imaging Augmented with Stent Reendothelialization Score

  • Florian Dubuisson
  • Claude Kauffmann
  • Pascal Motreff
  • Laurent Sarry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5761)

Abstract

The aim of this study is to automatically assess reendothelialization of stents at an accuracy of down to a few microns by analyzing endovascular optical coherence tomography (OCT) sequences. Vessel wall and struts are automatically detected and complete distance map is then computed from sparse distances measured between wall and struts by thin-plate spline (TPS) interpolation. A reendothelialization score is mapped onto the geometry of the coronary artery segment. Accuracy and robustness are increased by taking into account the inhomogeneity of datapoints and integrating in the same framework orthogonalized forward selection of support points, optimal selection of regularization parameters by generalized cross-validation (GCV) and rejection of detection outliers. The comparison against manual expert measurements for a phantom study and 12 in vivo stents demonstrates no significant discordance with variability of the order of the strut thickness.

Keywords

Optical Coherence Tomography Support Point Optical Coherence Tomography Image Neointimal Hyperplasia Active Contour Model 
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.

References

  1. 1.
    Pinto, T.L., Waksman, R.: Clinical applications of optical coherence tomography. J. Interv. Cardiol. 19, 566–573 (2006)CrossRefGoogle Scholar
  2. 2.
    Chen, B.X., Ma, F.Y., Luo, W., Ruan, J.H., Xie, W.L., Zhao, X.Z., Sun, S.H., Guo, X.M., Wang, F., Tian, T., Chu, X.W.: Neointimal coverage of bare-metal and sirolimus-eluting stents evaluated with optical coherence tomography. Heart 94, 566–570 (2008)CrossRefGoogle Scholar
  3. 3.
    Bonnema, G.T., Cardinal, K.O., McNally, J.B., Williams, S.K., Barton, J.K.: Assessment of blood vessel mimics with optical coherence tomography. J. Biomed. Opt. 12, 24018 (2007)CrossRefGoogle Scholar
  4. 4.
    Tanimoto, S., Rodriguez-Granillo, G., Barlis, P., de Winter, S., Bruining, N., Hamers, R., Knappen, M., Verheye, S., Serruys, P.W., Regar, E.: A novel approach for quantitative analysis of intracoronary optical coherence tomography: high inter-observer agreement with computer-assisted contour detection. Catheter Cardiovasc. Interv. 72, 228–235 (2008)CrossRefGoogle Scholar
  5. 5.
    Otsu, N.: A threshold selection method from gray-scale histogram. IEEE Trans. Systems, Man, and Cybernetics 8, 62–66 (1978)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kauffmann, C., Godbout, B., de Guise, J.A.: Simplified active contour model applied to bone structure segmentation in digital radiographs. In: Medical Imaging, SPIE International Symposium, pp. 663–672 (1998)Google Scholar
  7. 7.
    Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Patt. Anal. Mach. Intell. 21, 476–480 (1999)CrossRefGoogle Scholar
  8. 8.
    Donato, G., Belongie, S.: Approximate thin plate spline mappings. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 21–31. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Orr, M.J.L.: Regularisation in the selection of radial basis function centres. Neural Computation 7, 606–623 (1995)CrossRefGoogle Scholar
  10. 10.
    Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning for radial basis function network. IEEE Trans. Neural Networks 2, 302–309 (1991)CrossRefGoogle Scholar
  11. 11.
    Wahba, G.: Spline Models for Observational Data. In: CBSM-NSF Regional Conf. Ser. Appl. Math., vol. 59. SIAM, Philadelphia (1990)Google Scholar
  12. 12.
    Bartoli, A.: Maximizing the predictivity of smooth deformable image warps through cross-validation. J. Math. Imaging Vis. 31, 133–145 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lorensen, W.E., Cline, H.E.: Marching Cubes: a high resolution 3D surface reconstruction algorithm. Computer Graphics 21, 163–169 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Florian Dubuisson
    • 1
  • Claude Kauffmann
    • 2
  • Pascal Motreff
    • 1
    • 3
  • Laurent Sarry
    • 1
  1. 1.ERIM, Faculty of MedicineUniversity of AuvergneClermont-FerrandFrance
  2. 2.Department of Medical ImagingNotre-Dame Hospital, CHUMMontrealCanada
  3. 3.Department of CardiologyGabriel Montpied HospitalClermont-FerrandFrance

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