Image-based electrode array tracking for epicardial electrophysiological mapping in minimally invasive arrhythmia surgery

  • Hongho KimEmail author
  • Paul de Lange
  • Takehiro Ando
  • Sanghyun Joung
  • Kazuhiro Taniguchi
  • Hongen Liao
  • Shunei Kyo
  • Minoru Ono
  • Shinichi Takamoto
  • Etsuko Kobayashi
  • Ichiro Sakuma
Original Article



Electrophysiological mapping is effective in realizing a precise minimally invasive arrhythmia surgery. Recently, an epicardial electrophysiological mapping system for minimally invasive arrhythmia surgery was reported. The system requires a small electrode array, a tracking system and a global mapping algorithm. The optical tracking system employed in the research requires line of sight and complicated configuration. This paper proposes a new tracking method for locating an electrode array.


We developed a small electrode array and optical markers. Center points of respective optical markers and the electrode array are tracked via an endoscopic stream and calculated in image space. The orientation of the electrode array is calculated using the dot product between the vector joining two center points of two upper optical markers and the vector joining two end points of the longest edge of the electrode array.


Mean tracking errors of position and orientation of the electrode array were 0.51 mm and 0.64°, respectively. And the processing time was constant at 46 ms per frame. Our method could successfully track the electrode array on the epicardium during in vivo experiment and a global epicardial electrophysiological map was reconstructed from separately measured epicardial electrograms by the small electrode array.


An image-based tracking method for locating an electrode array was proposed. Tracking accuracy, processing time and applicability to surgical environment of our method proved to be acceptable. Consequently, our method enables the electrode array tracking system to be simplified with no separate tracking system.


Arrhythmia Minimally invasive Maze procedure Electrophysiological map Cardiac tracking Object tracking system 


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  1. 1.
    Scheinman MM, Morady F, Hess DS, Gonzales R (1982) Catheter-induced ablation of the atrioventricular junction to control refractory supraventricular arrhythmias. JAMA 248: 851–855CrossRefPubMedGoogle Scholar
  2. 2.
    Cox JL (2000) The minimally invasive Maze-III procedure. Oper Tech Thorac Cardiovasc Surg 5: 79–92CrossRefGoogle Scholar
  3. 3.
    van Brakel TJ, Bolotin G, Nifong LW et al (2005) Robot-assisted epicardial ablation of the pulmonary veins: is a completed isolation necessary? Eur Heart J 26: 1321–1326CrossRefPubMedGoogle Scholar
  4. 4.
    Schilling RJ, Peters NS, Davies DW (1999) Feasibility of a noncontact catheter for endocardial mapping of human ventricular tachycardia. Circulation 99: 2543–2552PubMedGoogle Scholar
  5. 5.
    Friedman PA (2002) Novel mapping techniques for cardiac electrophysiology. Heart 87: 575–582CrossRefPubMedGoogle Scholar
  6. 6.
    Paul T, Windhagen-Mahnert B, Kriebel T, Bertram H, Kaulitz R, Korte T, Niehaus M, Tebbenjohanns J (2001) Atrial reentrant tachycardia after surgery of congenital heart disease: endocardial mapping and radiofrequency catheter ablation using a novel non-contact mapping system. Circulation 103: 2266–2271PubMedGoogle Scholar
  7. 7.
    Cox JL (2004) Surgical treatment of atrial fibrillation: a review. Europace 5: 20–29CrossRefGoogle Scholar
  8. 8.
    Takata Y (2009) Global epicardial electrophysiological mapping with local multi-channel electrode array. JSCAS 11(1):25–38 (in Japanese)Google Scholar
  9. 9.
    Raab F, Blood E, Steiner T, Jones T (1979) Magnetic position and orientation tracking system. IEEE Trans Aerosp Electron Syst AES-15(5): 709–718CrossRefGoogle Scholar
  10. 10.
    Nixon MA, McCallum BC, Fright WR, Price NB (1998) The effects of metals and interfering fields on electromagnetic trackers. Presence 7: 204–218CrossRefGoogle Scholar
  11. 11.
    Lowe D (1992) Robust model-based motion tracking through the integration of search and estimation. Int J Comput Vis 8(2): 113–122CrossRefGoogle Scholar
  12. 12.
    Harris C (1992) Tracking with rigid models. In: Blake A, Yuille A (eds) Active vision. MIT Press, Cambridge, pp 59–74Google Scholar
  13. 13.
    Gennery D (1992) Visual tracking of known three-dimensional objects. Int J Comput Vis 7(3): 243–270CrossRefGoogle Scholar
  14. 14.
    Wang H, Li Z (1997) Tracking a rigid object in 3D from a single camera. Proc SPIE 3185: 78. doi: 10.1117/12.284031 CrossRefGoogle Scholar
  15. 15.
    Burschka D, Corso JJ, Dewan M, Hager GD, Lau W, Li M, Lin H Marayong P, Ramey N (2004) Navigating inner space: 3-d assistance for minimally invasive surgery. In: Workshop advances in robot vision, IEEE/RSJ internationl conference on intelligent robots and systems, Sendai, Japan, pp 67–78Google Scholar
  16. 16.
    Casals A, Amat J, Laporte E (1996) Automatic guidance of an assistant robot in laparoscopic surgery. In: Proceedings 1996 IEEE international conference on robotics and automation, vol 1, pp 895–900Google Scholar
  17. 17.
    Wei GQ, Arbter K, Hirzinger G (1997) Real-time visual servoing for laparoscopic surgery. Controlling robot motion with color image segmentation. IEEE Eng Med Biol Mag 16(1): 40–45CrossRefPubMedGoogle Scholar
  18. 18.
    Tonet O, Ramesh TU, Megali G, Dario P (2005) Image analysis-based approach for localization of endoscopic tools. In: Proceedings of the 2nd international conference on computer-aided medical interventions: tools and applications, pp 60–65Google Scholar
  19. 19.
    Voros S, Orvain E, Cinquin P (2006) Automatic detection of instruments in laparoscopic images: a first step towards high level command of robotized endoscopic holders. In: BIOROB 2006 1st IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, PisaGoogle Scholar
  20. 20.
    de Lange P (2009) Condensation algorithm based epicardium tracking. J JSCAS 11(3): 312–313Google Scholar
  21. 21.
    Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11): 1330–1334CrossRefGoogle Scholar
  22. 22.
    Isard M, Blake A (1998) Condensation—conditional density propagation for visual tracking. Int J Comput Vis 29: 5–28CrossRefGoogle Scholar
  23. 23.
    Nummiaro K, Koller-Meier E, Gool LV (2002) An adaptive color-based particle filter. Image Vis Comput 21(1): 99–110CrossRefGoogle Scholar
  24. 24.
    Pratt W (1991) Digital image processing, 2nd edn. Wiley, New YorkGoogle Scholar
  25. 25.
    Nakamura Y, Kishi K, Kawakami H (2001) Heartbeat synchronization for robotic cardiac surgery. In: Proceedings of the 2001 IEEE international conference on robotics & automation, pp 2014–2019Google Scholar

Copyright information

© CARS 2010

Authors and Affiliations

  • Hongho Kim
    • 1
    Email author
  • Paul de Lange
    • 1
  • Takehiro Ando
    • 1
  • Sanghyun Joung
    • 1
  • Kazuhiro Taniguchi
    • 1
  • Hongen Liao
    • 1
  • Shunei Kyo
    • 2
  • Minoru Ono
    • 2
  • Shinichi Takamoto
    • 3
  • Etsuko Kobayashi
    • 1
  • Ichiro Sakuma
    • 1
  1. 1.Graduate School of EngineeringThe University of TokyoTokyoJapan
  2. 2.Graduate School of MedicineThe University of TokyoTokyoJapan
  3. 3.Mitsui Memorial HospitalTokyoJapan

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