Advertisement

The Effect of Automated Marker Detection on in Vivo Volumetric Stent Reconstruction

  • Gert Schoonenberg
  • Pierre Lelong
  • Raoul Florent
  • Onno Wink
  • Bart ter Haar Romeny
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

New drug eluting stents are less radiopaque than bare metal stents and therefore difficult to see with conventional X-ray coronary angiography. 2D StentBoost and intravascular ultrasound (IVUS) are routinely used to evaluate stent deployment and vessel apposition during a percutaneous coronary intervention. IVUS images give cross-sectional information about the stent lumen and surrounding tissue. 2D StentBoost is a boosted angiogram sequence and visualizes the geometry of the deployed stent from a fixed viewing direction. Three-dimensional motion compensated volumetric stent reconstruction has been developed to give insight into the 3D geometry of the stent. Markers on the balloon wire are used to motion compensate cardiac rotational angiography acquisitions. In this paper we present the effect of automated marker detection on in vivo volumetric cardiac stent reconstructions. Automated or semi-automated marker detection reduces user interaction, potentially reduces total processing time, and increases detection results which leads to higher quality of stent reconstructions.

Keywords

angiography percutaneous coronary interventions marker detection motion compensation coronary stent reconstruction 

References

  1. 1.
    Mishell, J.M., Vakharia, K.T., Portset, T.A., Yeghiazarians, Y., Michaels, A.D.: Determination of Adequate Coronary Stent Expansion Using StentBoost, a Novel Fluoroscopic Image Processing Technique. Catheter Cardio. Inte. 69, 84–93 (2007)CrossRefGoogle Scholar
  2. 2.
    Chen, S.J., Carroll, J.D.: 3-D Reconstruction of Coronary Arterial Tree to Optimize Angiographic Visualization. IEEE Trans. Med. Imag. 19(4), 318–336 (2000)CrossRefGoogle Scholar
  3. 3.
    Feldkamp, L., Davis, L., Kress, J.: Practical cone-beam algorithm. J. Opt. Soc. Amer. A 1(6), 612–619 (1984)CrossRefGoogle Scholar
  4. 4.
    Perrenot, B., Vaillant, R., Prost, R., Finet, G., Douek, P., Peyrin, F.: Motion Correction for Coronary Stent Reconstruction From Rotational X-ray Projection Sequences. IEEE Trans. Med. Imag. 26(10), 1412–1423 (2007)CrossRefGoogle Scholar
  5. 5.
    Movassaghi, B., Schaefer, D., Grass, M., et al.: 3D Reconstruction of Coronary Stents in Vivo Based on Motion Compensated X-Ray Angiograms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 177–184. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Schaefer, D., Movassaghi, B., Grass, M., et al.: Three-dimensional reconstruction of coronary stents in vivo based on motion compensated X-ray angiography. In: Cleary, K.R., Miga, M.I. (eds.) Proceedings of SPIE, Volume 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, March 22, 2007, 65091M (2007)Google Scholar
  7. 7.
    Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 79–116 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gert Schoonenberg
    • 1
    • 3
  • Pierre Lelong
    • 2
  • Raoul Florent
    • 2
  • Onno Wink
    • 1
  • Bart ter Haar Romeny
    • 3
  1. 1.Philips Healthcare, X-Ray Predevelopment, BestThe Netherlands
  2. 2.Philips France, Philips Research ParisParisFrance
  3. 3.Department of Biomedical Engineering, Division Biomedical Imaging and ModelingTechnische Universiteit EindhovenEindhovenThe Netherlands

Personalised recommendations