Advertisement

Coronary Motion Modelling for Augmented Reality Guidance of Endoscopic Coronary Artery Bypass

  • Michael Figl
  • Daniel Rueckert
  • David Hawkes
  • Roberto Casula
  • Mingxing Hu
  • Ose Pedro
  • Dong Ping Zhang
  • Graeme Penney
  • Fernando Bello
  • Philip Edwards
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5104)

Abstract

The overall aim of our project is to guide totally endoscopic coronary artery bypass. This requires construction of a 4D preoperative model of the coronary arteries and myocardium. The model must be aligned with the endoscopic view of the patient’s beating heart and presented to the surgeon using augmented reality. We propose that the model can be constructed from coronary CT. Segmentation can be performed for one phase of the cardiac cycle only and propagated to the others using non-rigid registration. We have compared the location of the coronaries produced by this method to hand segmentation.

Registration of the model to the endoscopic view of the patient is achieved in two phases. Temporal registration is performed by identification of corresponding motion between model and video. Then we calculate photo-consistency between the two da Vinci endoscope views and average over the frames of the motion model. This has been shown to improve the shape of the cost function. Phantom results are presented.

The model can then be transformed to the calibrated endoscope view and overlaid using two video mixers.

Keywords

Augmented Reality Camera Calibration Beating Heart Feature Tracking Endoscopic View 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dogan, S., Aybek, T., Andressen, E., Byhahn, C., Mierdl, S., Westphal, K., Matheis, G., Moritz, A., Wimmer-Greinecker, G.: Totally endoscopic coronary artery bypass grafting on cardiopulmonary bypass with robotically enhanced telemanipulation: Report of forty-five cases. J. Thorac. Cardiovasc. Surg. 123, 1125–1131 (2002)CrossRefGoogle Scholar
  2. 2.
    Falk, V., Diegeler, A., Walther, T., Banusch, J., Brucerius, J., Raumans, J., Autschbach, R., Mohr, F.W.: Total endoscopic computer enhanced coronary artery bypass grafting. Eur. J. Cardio-Thorac. Surg. 17, 38–45 (2000)CrossRefGoogle Scholar
  3. 3.
    Wierzbicki, M., Drangova, G., Guiraudon, G., Peters, T.M.: Validation of dynamic heart models obtained using non-linear registration for virtual reality training, planning, and guidance of minimally invasive cardiac surgeries. Med. Image Anal. 8, 387–401 (2004)CrossRefGoogle Scholar
  4. 4.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  5. 5.
    Chandrashekara, R., Mohiaddin, R., Razavi, R., Rueckert, R.: Nonrigid image registration with subdivision lattices: Application to cardiac mr image analysis. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 335–342. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    Perperidis, D., Mohiaddin, R., Edwards, P., Rueckert, D.: Hill: Segmentation of cardiac MR and CT image sequences using model-based registration of a 4D statistical model. In: Proc. SPIE Medical Imaging 2007, vol. 6512 (2007)Google Scholar
  7. 7.
    Blackall, J.M., Penney, G.P., King, A.P., Hawkes, D.J.: Alignment of sparse freehand 3-d ultrasound with preoperative images of the liver using models of respiratory motion and deformation. IEEE Trans. Med. Imaging 24, 1405–1416 (2005)CrossRefGoogle Scholar
  8. 8.
    McLeish, K., Hill, D.L.G., Atkinson, D., Blackall, J.M., Razavi, R.: A study of the motion and deformation of the heart due to respiration. IEEE Trans. Med. 21, 1142–1150 (2002)CrossRefGoogle Scholar
  9. 9.
    Stoyanov, D., Mylonas, G.P., Deligianni, F., Darzi, A., Yang, G.Z.: Soft-tissue motion tracking and structure estimation for robotic assisted mis procedures. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 139–146. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Clarkson, M.J., Rueckert, D., Hill, D.L.G., Hawkes, D.J.: Using photo-consistency to register 2d optical images of the human face to a 3D surface model. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1266–1280 (2001)CrossRefGoogle Scholar
  11. 11.
    Bouguet, J.: Camera calibration toolbox for matlab (2007), http://www.vision.caltech.edu/bouguetj
  12. 12.
    Szpala, S., Wierzbicki, M., Guiraudon, G., Peters, T.M.: Real-time fusion of endoscopic views with dynamic 3-d cardiac images: A phantom study. IEEE Trans. Med. Imaging 24, 1207–1215 (2005)CrossRefGoogle Scholar
  13. 13.
    Falk, V., Mourgues, F., Adhami, L., Jacobs, S., Thiele, H., Nitzsche, S., Mohr, F.W., Coste-Maniere, T.: Cardio navigation: Planning, simulation, and augmented reality in robotic assisted endoscopic bypass grafting. Ann. Thorac. Surg. 79, 2040–2048 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michael Figl
    • 1
  • Daniel Rueckert
    • 1
  • David Hawkes
    • 3
  • Roberto Casula
    • 4
  • Mingxing Hu
    • 3
  • Ose Pedro
    • 1
  • Dong Ping Zhang
    • 1
  • Graeme Penney
    • 5
  • Fernando Bello
    • 2
  • Philip Edwards
    • 1
    • 2
  1. 1.Department of ComputingImperial College LondonUK
  2. 2.Department of Biosurgery and Surgical TechnologyImperial College LondonUK
  3. 3.Centre of Medical Image ComputingUniversity College LondonUK
  4. 4.Cardiothoracic Surgery, St. Mary’s HospitalLondonUK
  5. 5.Division of Imaging SciencesKing’s College LondonUK

Personalised recommendations