Surgical Endoscopy

, Volume 30, Issue 2, pp 495–503 | Cite as

Electromagnetic organ tracking allows for real-time compensation of tissue shift in image-guided laparoscopic rectal surgery: results of a phantom study

  • M. Wagner
  • M. Gondan
  • C. Zöllner
  • J. J. Wünscher
  • F. Nickel
  • L. Albala
  • A. Groch
  • S. Suwelack
  • S. Speidel
  • L. Maier-Hein
  • B. P. Müller-Stich
  • H. G. KenngottEmail author



Laparoscopic resection is a minimally invasive treatment option for rectal cancer but requires highly experienced surgeons. Computer-aided technologies could help to improve safety and efficiency by visualizing risk structures during the procedure. The prerequisite for such an image guidance system is reliable intraoperative information on iatrogenic tissue shift. This could be achieved by intraoperative imaging, which is rarely available. Thus, the aim of the present study was to develop and validate a method for real-time deformation compensation using preoperative imaging and intraoperative electromagnetic tracking (EMT) of the rectum.


Three models were compared and evaluated for the compensation of tissue deformation. For model A, no compensation was performed. Model B moved the corresponding points rigidly to the motion of the EMT sensor. Model C used five nested linear regressions with increasing level of complexity to compute the deformation (C1–C5). For evaluation, 14 targets and an EMT organ sensor were fit into a silicone-molded rectum of the OpenHELP phantom. Following a computed tomography, the image guidance was initiated and the rectum was deformed in the same way as during surgery in a total of 14 experimental runs. The target registration error (TRE) was measured for all targets in different positions of the rectum.


The mean TRE without correction (model A) was 32.8 ± 20.8 mm, with only 19.6 % of the measurements below 10 mm (80.4 % above 10 mm). With correction, the mean TRE could be reduced using the rigid correction (model B) to 6.8 ± 4.8 mm with 78.7 % of the measurements being <10 mm. Using the most complex linear regression correction (model C5), the error could be reduced to 2.9 ± 1.4 mm with 99.8 % being below 10 mm.


In laparoscopic rectal surgery, the combination of electromagnetic organ tracking and preoperative imaging is a promising approach to compensating for intraoperative tissue shift in real-time.


Rectal cancer Laparoscopy Image-guided surgery Motion compensation 



This study was conducted within the Research Training Group 1126 Intelligent Surgery and the Transregional Collaborative Research Center 125 Cognition-guided Surgery, both funded by the German Research Foundation (DFG; Projects A01 and A02). The authors thank Ms. Béivin Pyne for careful review of the manuscript as a native speaker.


M. Wagner, M. Gondan, C. Zöllner, J. J. Wünscher, F. Nickel, L. Albala, A. Groch, S. Suwelack, S. Speidel, L. Maier-Hein, B. P. Müller-Stich and H. G. Kenngott have no conflicts of interest or financial ties to disclose.

Supplementary material

464_2015_4231_MOESM1_ESM.docx (144 kb)
Supplementary material 1 (DOCX 143 kb)


  1. 1.
    Jamali FR, Soweid AM, Dimassi H, Bailey C, Leroy J, Marescaux J (2008) Evaluating the degree of difficulty of laparoscopic colorectal surgery. Arch Surg 143:762–767. doi: 10.1001/archsurg.143.8.762 CrossRefPubMedGoogle Scholar
  2. 2.
    Schulz C, Waldeck S, Mauer UM (2012) Intraoperative image guidance in neurosurgery: development, current indications, and future trends. Radiol Res Pract. doi: 10.1155/2012/197364 PubMedCentralPubMedGoogle Scholar
  3. 3.
    Justice JM, Orlandi RR (2012) An update on attitudes and use of image-guided surgery. Int Forum Allergy Rhinol 2:155–159. doi: 10.1002/alr.20107 CrossRefPubMedGoogle Scholar
  4. 4.
    Hetaimish BM, Khan MM, Simunovic N, Al-Harbi HH, Bhandari M, Zalzal PK (2012) Meta-analysis of navigation vs conventional total knee arthroplasty. J Arthroplasty 27:1177–1182. doi: 10.1016/j.arth.2011.12.028 CrossRefPubMedGoogle Scholar
  5. 5.
    Clifford MA, Banovac F, Levy E, Cleary K (2002) Assessment of hepatic motion secondary to respiration for computer assisted interventions. Comput Aided Surg 7:291–299. doi: 10.1002/igs.10049 CrossRefPubMedGoogle Scholar
  6. 6.
    Wysocka B, Kassam Z, Lockwood G, Brierley J, Dawson LA, Buckley CA, Jaffray D, Cummings B, Kim J, Wong R, Ringash J (2010) Interfraction and respiratory organ motion during conformal radiotherapy in gastric cancer. Int J Radiat Oncol Biol Phys 77:53–59. doi: 10.1016/j.ijrobp.2009.04.046 CrossRefPubMedGoogle Scholar
  7. 7.
    Zijlmans M, Langø T, Hofstad EF, Van Swol CFP, Rethy A (2012) Navigated laparoscopy–liver shift and deformation due to pneumoperitoneum in an animal model. Minim Invasive Ther Allied Technol 21:241–248. doi: 10.3109/13645706.2012.665805 CrossRefPubMedGoogle Scholar
  8. 8.
    Kenngott HG, Wagner M, Gondan M, Nickel F, Nolden M, Fetzer A, Weitz J, Fischer L, Speidel S, Meinzer H-P, Böckler D, Büchler MW, Müller-Stich BP (2014) Real-time image guidance in laparoscopic liver surgery: first clinical experience with a guidance system based on intraoperative CT imaging. Surg Endosc 28:933–940. doi: 10.1007/s00464-013-3249-0 CrossRefPubMedGoogle Scholar
  9. 9.
    Kenngott HG, Neuhaus J, Müller-Stich BP, Wolf I, Vetter M, Meinzer H-P, Köninger J, Büchler MW, Gutt CN (2008) Development of a navigation system for minimally invasive esophagectomy. Surg Endosc 22:1858–1865. doi: 10.1007/s00464-007-9723-9 CrossRefPubMedGoogle Scholar
  10. 10.
    Nolden M, Zelzer S, Seitel A, Wald D, Müller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer H-P, Wolf I (2013) The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development. Int J Comput Assist Radiol Surg 8:607–620. doi: 10.1007/s11548-013-0840-8 CrossRefPubMedGoogle Scholar
  11. 11.
    Kenngott HG, Wünscher JJ, Wagner M, Preukschas A, Wekerle AL, Neher P, Suwelack S, Speidel S, Nickel F, Oladokun D, Maier-Hein L, Dillmann R, Meinzer HP, Müller-Stich BP (2015) OpenHELP (Heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool. Surg Endosc. doi: 10.1007/s00464-015-4094-0 Google Scholar
  12. 12.
    Franz AM, Haidegger T, Birkfellner W, Cleary K, Peters TM, Maier-Hein L (2014) Electromagnetic tracking in medicine—a review of technology, validation, and applications. IEEE Trans Med Imaging 33:1702–1725. doi: 10.1109/TMI.2014.2321777 CrossRefPubMedGoogle Scholar
  13. 13.
    Kenngott HG, Wegner I, Neuhaus J, Nickel F, Fischer L, Gehrig T, Meinzer HP, Müller-Stich BP (2013) Magnetic tracking in the operation room using the da Vinci(®) telemanipulator is feasible. J Robot Surg 7:59–64. doi: 10.1007/s11701-012-0347-2 PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Horn BKP, Hilden HM, Negahdaripour S (1988) Closed-form solution of absolute orientation using orthonormal matrices. J Opt Soc Am A 5:1127–1135. doi: 10.1364/JOSAA.5.001127 CrossRefGoogle Scholar
  15. 15.
    R Core Team (2012) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  16. 16.
    Peterhans M, Oliveira T, Banz V, Candinas D, Weber S (2012) Computer-assisted liver surgery: clinical applications and technological trends. Crit Rev Biomed Eng 40:199–220CrossRefPubMedGoogle Scholar
  17. 17.
    Nicolau S, Soler L, Mutter D, Marescaux J (2011) Augmented reality in laparoscopic surgical oncology. Surg Oncol 20:189–201. doi: 10.1016/j.suronc.2011.07.002 CrossRefPubMedGoogle Scholar
  18. 18.
    Crum WR, Hartkens T, Hill DLG (2004) Non-rigid image registration: theory and practice. Br J Radiol 77(Spec No 2):S140–S153CrossRefPubMedGoogle Scholar
  19. 19.
    Nakamoto M, Nakada K, Sato Y, Konishi K, Hashizume M, Tamura S (2008) Intraoperative magnetic tracker calibration using a magneto-optic hybrid tracker for 3-D ultrasound-based navigation in laparoscopic surgery. IEEE Trans Med Imaging 27:255–270. doi: 10.1109/TMI.2007.911003 CrossRefPubMedGoogle Scholar
  20. 20.
    Khan MF, Dogan S, Maataoui A, Wesarg S, Gurung J, Ackermann H, Schiemann M, Wimmer-Greinecker G, Vogl TJ (2006) Navigation-based needle puncture of a cadaver using a hybrid tracking navigational system. Invest Radiol 41:713–720. doi: 10.1097/ CrossRefPubMedGoogle Scholar
  21. 21.
    Feuerstein M, Reichl T, Vogel J, Traub J, Navab N (2009) Magneto-optical tracking of flexible laparoscopic ultrasound: model-based online detection and correction of magnetic tracking errors. IEEE Trans Med Imaging 28:951–967. doi: 10.1109/TMI.2008.2008954 CrossRefPubMedGoogle Scholar
  22. 22.
    Markert M, Koschany A, Lueth T (2010) Tracking of the liver for navigation in open surgery. Int J Comput Assist Radiol Surg 5:229–235. doi: 10.1007/s11548-009-0395-x CrossRefPubMedGoogle Scholar
  23. 23.
    Nakamoto M, Ukimura O, Gill IS, Mahadevan A, Miki T, Hashizume M, Sato Y (2008) Realtime organ tracking for endoscopic augmented reality visualization using miniature wireless magnetic tracker. In: Proceedings of the 4th international workshop on medical imaging and augmented reality. Springer, Berlin, Heidelberg, pp 359–366Google Scholar
  24. 24.
    Maier-Hein L, Tekbas A, Seitel A, Pianka F, Müller SA, Satzl S, Schawo S, Radeleff B, Tetzlaff R, Franz AM, Müller-Stich BP, Wolf I, Kauczor H-U, Schmied BM, Meinzer H-P (2008) In vivo accuracy assessment of a needle-based navigation system for CT-guided radiofrequency ablation of the liver. Med Phys 35:5385–5396CrossRefPubMedGoogle Scholar
  25. 25.
    Beller S, Eulenstein S, Lange T, Hünerbein M, Schlag PM (2009) Upgrade of an optical navigation system with a permanent electromagnetic position control: a first step towards “navigated control” for liver surgery. J Hepatobiliary Pancreat Surg 16:165–170. doi: 10.1007/s00534-008-0040-z CrossRefPubMedGoogle Scholar
  26. 26.
    Rassweiler JJ, Müller M, Fangerau M, Klein J, Goezen AS, Pereira P, Meinzer H-P, Teber D (2012) iPad-assisted percutaneous access to the kidney using marker-based navigation: initial clinical experience. Eur Urol 61:628–631. doi: 10.1016/j.eururo.2011.12.024 CrossRefPubMedGoogle Scholar
  27. 27.
    Heald R, Ryall RD (1986) Recurrence and survival after total mesorectal excision for rectal cancer. The Lancet 327:1479–1482. doi: 10.1016/S0140-6736(86)91510-2 CrossRefGoogle Scholar
  28. 28.
    Shekhar R, Dandekar O, Bhat V, Philip M, Lei P, Godinez C, Sutton E, George I, Kavic S, Mezrich R, Park A (2010) Live augmented reality: a new visualization method for laparoscopic surgery using continuous volumetric computed tomography. Surg Endosc 24:1976–1985. doi: 10.1007/s00464-010-0890-8 CrossRefPubMedGoogle Scholar
  29. 29.
    Nickel F, Kenngott HG, Neuhaus J, Sommer CM, Gehrig T, Kolb A, Gondan M, Radeleff BA, Schaible A, Meinzer H-P, Gutt CN, Müller-Stich B-P (2013) Navigation system for minimally invasive esophagectomy: experimental study in a porcine model. Surg Endosc 27:3663–3670. doi: 10.1007/s00464-013-2941-4 CrossRefPubMedGoogle Scholar
  30. 30.
    Maier-Hein L, Franz AM, Birkfellner W, Hummel J, Gergel I, Wegner I, Meinzer H-P (2012) Standardized assessment of new electromagnetic field generators in an interventional radiology setting. Med Phys 39:3424–3434. doi: 10.1118/1.4712222 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • M. Wagner
    • 1
  • M. Gondan
    • 2
  • C. Zöllner
    • 3
  • J. J. Wünscher
    • 1
  • F. Nickel
    • 1
  • L. Albala
    • 1
  • A. Groch
    • 3
  • S. Suwelack
    • 4
  • S. Speidel
    • 4
  • L. Maier-Hein
    • 3
  • B. P. Müller-Stich
    • 1
  • H. G. Kenngott
    • 1
    Email author
  1. 1.Department of SurgeryHeidelberg University HospitalHeidelbergGermany
  2. 2.Department of PsychologyUniversity of CopenhagenCopenhagenDenmark
  3. 3.Junior Group Computer-Assisted InterventionsGerman Cancer Research CenterHeidelbergGermany
  4. 4.Institute for Anthropomatics and RoboticsKarlsruhe Institute of TechnologyKarlsruheGermany

Personalised recommendations