Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

  • Evgeniya Bogatyrenko
  • Pascal Pompey
  • Uwe D. Hanebeck
Original Article

Abstract

Purpose

Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions.

Methods

A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart. The main advantage of the model is that it is more realistic than most standard heart models. Additionally, no explicit matching between the measurements and the model is required. The application of meshless methods significantly reduces the complexity of physics-based tracking.

Results

Based on the stochastic physical model of the heart surface, this approach considers the motion of the intervention area and is robust to occlusions and reflections. The tracking algorithm is evaluated in simulations and experiments on an artificial heart. Providing higher accuracy than the standard model-based methods, it successfully copes with occlusions and provides high performance even when all measurements are not available.

Conclusions

Combining the physical and stochastic description of the heart surface motion ensures physically correct and accurate prediction. Automatic initialization of the physics-based cardiac motion tracking enables system evaluation in a clinical environment.

Keywords

Beating heart surgery Tracking Model-based estimation Trinocular camera system Computer vision 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chomiac P, Bose S, Page M (2001) Beating heart bypass surgery. Medtronic Cardiac Surgery, Grand RapidsGoogle Scholar
  2. 2.
    Jacobs S, Holzhey D, Kiaii BB, Onnasch JF, Walther T, Mohr FW, Falk V (2003) Limitations for manual and telemanipulator-assisted motion tracking—implications for endoscopic beating-heart surgery. Ann Thorac Surg 76: 2029–2036PubMedCrossRefGoogle Scholar
  3. 3.
    Falk V (2002) Manual control and tracking—a human factor analysis relevant for beating heart surgery. Ann Thorac Surg 74: 624–628PubMedCrossRefGoogle Scholar
  4. 4.
    Çavuşoǧlu MC, Rotella J, Newman WS, Choi S, Ustin J, Sastry SS (2005) Control algorithms for active relative motion cancelling for robotic assisted off-pump coronary artery bypass graft surgery. In: Proceedings of the 12th international conference on advanced robotics (ICAR 2005), Seattle, USA, pp 431–436Google Scholar
  5. 5.
    Nyquist H (1928) Certain topics in telegraph transmission theory. Proc IEEE 47: 617–644Google Scholar
  6. 6.
    Nakamura Y, Kishi K, Kawakami H (2001) Heartbeat synchronization for robotic cardiac surgery. In: Proceedings of the IEEE international conference on robotics and automation (ICRA 2001), Seoul, Korea, pp 2014–2019Google Scholar
  7. 7.
    Stoyanov D, Mylonas GP, Deligianni F, Darzi A, Yang GZ (2005) Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures. In: Proceedings of the medical image computing and computer assisted interventions (MICCAI 2005), vol 2, pp 139–146Google Scholar
  8. 8.
    Ramey NA, Corso JJ, Lau WW, Burschka D, Hager GD (2004) Real-time 3D surface tracking and its applications. In: Proceedings of the 2004 conference on computer vision and pattern recognition workshop (CVPRW 2004)Google Scholar
  9. 9.
    Lau WW, Ramey NA, Corso JJ, Thakor NV, Hager GD (2004) Stereo-based endoscopic tracking of cardiac surface deformation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI 2004), pp 494–501Google Scholar
  10. 10.
    Richa R, Poignet P, Liu C (2008) Efficient 3D tracking for motion compensation in beating heart surgery. In: Proceeding of the medical image computing and computer-assisted intervention (MICCAI 2008), vol 5242, pp 684–691Google Scholar
  11. 11.
    Sauvée M, Poignet P, Triboulet J, Dombre E, Malis E, Demaria R (2006) 3D heart motion estimation using endoscopic monocular vision system. In: Proceeding of the IFAC symposium on modeling and control in biomedical systems, pp 1–6Google Scholar
  12. 12.
    Noce A, Triboulet J, Poignet P (2007) Efficient tracking of the heart using texture. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society (EMBS 2007), Lyon, France, pp 4480–4483Google Scholar
  13. 13.
    Sauvée M, Noce A, Poignet P, Triboulet J, Dombre E (2007) Three-dimensional heart motion estimation using endoscopic monocular vision system: from artificial landmarks to texture analysis. In: Proceeding of the IFAC symposium on modeling and control in biomedical systems, 3, vol 2, pp 199–207Google Scholar
  14. 14.
    Ortmaier T, Groeger M, Boehm DH, Falk V, Hirzinger G (2005) Motion estimation in beating heart surgery. IEEE Trans Biomed Eng 52(10): 1729–1740PubMedCrossRefGoogle Scholar
  15. 15.
    Bebek Ö, Çavuşoǧlu MC (2007) Intelligent control algorithms for robotic-assisted beating heart surgery. IEEE Trans Robot 23(3): 468–480CrossRefGoogle Scholar
  16. 16.
    Richa R, Poignet P, Liu C (2008) Deformable motion tracking of the heart surface. In: Proceedings of the 2008 IEEE international conference on intelligent robots and systems (IROS 2008)Google Scholar
  17. 17.
    Bader T, Wiedemann A, Roberts K, Hanebeck UD (2007) Model–based motion estimation of elastic surfaces for minimally invasive cardiac surgery. In: Proceedings of the 2007 IEEE international conference on robotics and automation (ICRA 2007), Rome, Italy, pp 2261–2266. doi:10.1109/ROBOT.2007.363656
  18. 18.
    Bogatyrenko E, Hanebeck UD, Szabo G (2009) Heart surface motion estimation framework for robotic surgery employing meshless methods. In: Proceedings of the 2009 IEEE/RSJ international conference on intelligent robots and systems (IROS 2009)Google Scholar
  19. 19.
    Shi P, Liu H (2002) Stochastic finite element framework for cardiac kinematics function and material property analysis. In: Proceedings of the medical image computing and computer-assisted intervention (MICCAI 2002), pp 634–641Google Scholar
  20. 20.
    Sermesant M, Moireau P, Camara O, Sainte-Marie J, Andriantsimiavona R, Cimrman R, Hill D, Chapelle D, Razavi R (2006) Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Funct Imaging Model Heart 10: 642–656Google Scholar
  21. 21.
    Hartley R, Zisserman A (2000) Multiple view geometry in computer vision. Cambridge University Press, CambridgeGoogle Scholar
  22. 22.
    Mulligan J, Isler V, Daniilidis K (2001) Trinocular stereo: a real-time algorithm and its evaluation. Int J Comput Vis 27: 51–61Google Scholar
  23. 23.
    Zahorec R, Holoman M (1997) Transatrial access for left atrial pressure monitoring in cardiac surgery patients. Eur J Cardio-Thorac Surg 11: 379–380CrossRefGoogle Scholar
  24. 24.
    Atluri SN, Han ZD, Liu HT (2006) Meshless local Petrov-Galerkin (MLPG) mixed collocation method for elasticity problems. Tech Sci Press CMES 14(3): 141–152Google Scholar
  25. 25.
    Liu GR (2003) Mesh free methods: moving beyond the finite element methods. CRC Press, Boca RatonGoogle Scholar
  26. 26.
    Liu GR (2005) An introduction to meshfree methods and their programming. Springer, DordrechtGoogle Scholar
  27. 27.
    Uciński D (2005) Optimal measurement methods for distributed parameter system identification. CRC Press, Boca RatonGoogle Scholar
  28. 28.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82: 35–45Google Scholar
  29. 29.
    Yuen SG, Novotny PM, Howe RD (2008) Quasiperiodic predictive filtering for robot-assisted beating heart surgery. In: Proceeding of the IEEE international conference on robotics and automation (ICRA 2008), Pasadena, CA, USA, pp 3875–3880Google Scholar
  30. 30.
    Box G, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control. Prentice-Hall, Englewood CliffsGoogle Scholar
  31. 31.
    Cattina P, Daveb H, Gruenenfelder J, Szekelya G, Turin M, Zuend G (2004) Trajectory of coronary motion and its significance in robotic motion cancellation. Eur J Cardio-Thorac Surg 25: 786–790CrossRefGoogle Scholar
  32. 32.
    Allied Vision Technologies GmbH: PIKE technical manual V4.2.0 (2009)Google Scholar
  33. 33.
    Svoboda T, Martinec D, Pajdla T (2005) A convenient multi-camera self-calibration for virtual environments. Presence Teleoper Virt Environ 14(4): 1–26Google Scholar

Copyright information

© CARS 2010

Authors and Affiliations

  • Evgeniya Bogatyrenko
    • 1
  • Pascal Pompey
    • 1
  • Uwe D. Hanebeck
    • 1
  1. 1.Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for AnthropomaticsKarlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations