Computer assisted coronary intervention by use of on-line 3D reconstruction and optimal view strategy

  • S. -Y. James Chen
  • John D. Carroll
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


A novel method has been developed for on-line reconstruction of the 3D coronary arterial tree based on a pair of routine angiograms acquired from any two arbitrary viewing angles using a single-plane or biplane imaging system. An arterial segment of interest (e.g., coronary stenosis) is selected on the projection of reconstructed 3D coronary model. Afterwards, the process of optimal view strategy is employed resulting in foreshortening, overlap, and composite maps relative to the selected arterial segment by which any computer-generated projection associated with the gantry orientation can be previewed. By use of the three maps, the views with minimal foreshortening and vessel overlap for the selected arterial segment of interest can be determined to guide subsequent angiogram acquisitions for interventional procedure. More than 200 cases of coronary arterial systems have been reconstructed. A validation confirmed the accuracy of 3D length measurement to within RMS 3.1% error using 8 pairs of angiograms of intra-coronary catheter wire of 105 mm length.


Bifurcation Point Focal Spot Arterial Segment Vessel Centerline Coronary Arterial Tree 
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.


  1. 1.
    H.C. Kim, B.G. Min, T.S. Lee, et. al., “3D digital subtraction angiography,” IEEE Trans. Med. Imag., vol. MI-1, pp. 152–158, 1982.CrossRefGoogle Scholar
  2. 2.
    K.L. Parker, K.L. Pope, R. van Bree, et. al., “3-d reconstruction of moving arterial beds from digital subtraction angiography,” Comput. Biomed. Res., vol. 20, pp. 166–185, 1987.CrossRefPubMedGoogle Scholar
  3. 3.
    K. Kitamura, J.M. Tobis, and J. Sklansky, “Estimating the 3D skeletons and transverse areas of coronary arteries from biplane angiograms,” IEEE Trans. on MI, vol. MI-7, pp. 173–187, 1988.CrossRefGoogle Scholar
  4. 4.
    T. Saito, M. Misaki, K. Shirato, and T. Takishima, “Three-dimensional quantitative coronary angiography,” IEEE Trans. on Bio. Eng., vol. 37, no. 8, pp. 768–777, Aug. 1990.CrossRefGoogle Scholar
  5. 5.
    C.P. Pellot, A. Herment, M. Sigelle, P. Horain, H. Maitre, and P. Peronneau, “A 3d reconstruction of vascular structures from two x-ray angiograms using an adapted simulated annealing algorithm,” IEEE Tran. on Med. Imag., vol. 13, no. 1, pp. 49–60, Mar. 1994.Google Scholar
  6. 6.
    N. Guggenheim, P.A. Doriot, et al., “Spatial reconstruction of coronary arteries from angiographic images,” Phys. in Medic. & Biol, vol. 36, pp. 99–100, 1991.CrossRefGoogle Scholar
  7. 7.
    A. Wahle, E. Wellnhofer, I. Mugaragu, H.U. Sauer, H. Oswald, and E. Fleck, “Assessment of diffuse coronary artery disease by quantitative analysis of coronary morphology based upon 3-d reconstruction from biplane angiograms,” IEEE Trans. on MI, vol. 14, no. 2, pp. 230–241, 1995.CrossRefGoogle Scholar
  8. 8.
    S. Stansfield, “ANGI: A rule based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms,” IEEE Trans. on PAMI, vol. 8, no. 2, pp. 188–199, 1986.CrossRefGoogle Scholar
  9. 9.
    C. Smets, F. Vandewerf, P. Suetens, and A. Oosterlinck, “An expert system for the labeling and 3d reconstruction of the coronary arteries from two projections,” Int. J. Card. Img., vol. 5, no. 2–3, pp. 145–154, 1990.CrossRefGoogle Scholar
  10. 10.
    G. Coppini, M. Demi, R. Mennini, G. Valli, “3D knowledge driven reconstruction of coronary trees,” Medical & Bio. Eng. & Comp., pp. 535–542, 1991.Google Scholar
  11. 11.
    D. Delaere, C. Smets, P. Suetens, G. Marchal, and F. Van de Werf, “Knowledge-based system for the 3d reconstruction of blood vessels from two angiographic projections,” Med. Biol Eng. Comput., vol. 29, no. 6, pp. ns27–ns36, Nov. 1991.CrossRefPubMedGoogle Scholar
  12. 12.
    I. Liu and Y. Sun, “Fully automated reconstruction of 3-d vascular tree structures from two orthogonal views using computational algorithms and production rules”, Optical Engineering, vol. 31, no. 10, pp. 2197–2207, Oct. 1992.CrossRefGoogle Scholar
  13. 13.
    A. Rouge, C. Picard, D. Sanit-Felix, et al., “3-d coronary arteriography,” Int. J. of Card. Img., vol. 10, pp. 67–70, 1994.CrossRefGoogle Scholar
  14. 14.
    H. C. Longuet-Higgins, “A computer algorithm for reconstructing a scene from two projections,” Nature, vol. 293, no. 10, pp. 133–135, September 1981.CrossRefGoogle Scholar
  15. 15.
    R. Y. Tsai and T. S. Huang, “Uniqueness and estimation of 3D motion parameters of rigid objects with curved surfaces,” IEEE Trans. on PAMI, vol. 6, no. 1, pp. 13–27, Jan. 1984.CrossRefGoogle Scholar
  16. 16.
    J.Q. Fang, T.S. Huang, “Some experiments on estimating the 3-d motion parameters of a rigid body from two consecutive image frames,” IEEE Trans. on PAMI, vol. 6, pp. 547–554, Jan. 1984.Google Scholar
  17. 17.
    J. Philip, “Estimation of 3-d motion of rigid objects from noisy observations,” IEEE Trans. on PAMI, vol. 13, pp. 61–66, 1991.CrossRefGoogle Scholar
  18. 18.
    C. E. Metz and L. E. Fencil, “Determination of three-dimensional structure in biplane radiography without prior knowledge of the relationship between the two views: Theory,” Medical Physics, 16 (1), pp. 45–51, Jan/Feb 1989.CrossRefPubMedGoogle Scholar
  19. 19.
    L. E. Fencil and C. E. Metz, “Propagation and reduction of error in threedimensional structure determined from biplane views of unknown orientation,” Medical Physics, 17 (6), pp. 951–961, Nov/Dec 1990.CrossRefPubMedGoogle Scholar
  20. 20.
    J. Weng, T.S. Huang, and N. Ahuja, “A two-step approach to optimal motion and structure estimation,” Proc. IEEE Workshop Computer Vision, pp. 355–357, 1987.Google Scholar
  21. 21.
    J. Weng, N. Ahuja, and T. Huang, “Closed-form solution and maximum likelihood: a robust approach to motion and structure estimation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 381–386, 1988.Google Scholar
  22. 22.
    J. Weng, T.S Huang, N. Ahuja, “Motion and structure from two perspective view: algorithms, error analysis and error estimation,” IEEE Trans. on PAMI, vol. 11, pp. 451–476, Jan. 1989.CrossRefGoogle Scholar
  23. 23.
    J. Weng, N. Ahuja, T.S. Huang, “Optimal motion and structure estimation,” IEEE Trans. on PAMI, vol. 15, no. 9, pp. 864–884, 1993.CrossRefGoogle Scholar
  24. 24.
    S.-Y. J. Chen, K.R. Hoffmann, J.D. Carroll, “Three-dimensional reconstruction of coronary arterial tree based on biplane angiograms,” Proceedings of SPIE Medical Imaging: Image Processing, vol. 2710, Newport Beach, California, pp. 103–114, 1996CrossRefGoogle Scholar
  25. 25.
    S.-Y. J. Chen and C.E. Metz, “Improved determination of biplane imaging geometry from two projection images and its application to 3D reconstruction of coronary arterial trees,” Medical Physics, vol. 24, no. 5, May 1997, pp. 633–654.CrossRefPubMedGoogle Scholar
  26. 26.
    B.K.P. Horn, Robot Vision, The MIT Press, McGraw-Hill Book Company, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • S. -Y. James Chen
    • 1
  • John D. Carroll
    • 1
  1. 1.Cardiology Division, Department of MedicineUniversity of Colorado Health Sciences CenterUSA

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