Ray-casting based evaluation framework for haptic force feedback during percutaneous transhepatic catheter drainage punctures

  • Andre MastmeyerEmail author
  • Tobias Hecht
  • Dirk Fortmeier
  • Heinz Handels
Original Article



   Development of new needle insertion force feedback algorithms requires comparison with a gold standard method. A new evaluation framework was formulated and tested on needle punctures for percutaneous transhepatic catheter drainage (PTCD).


   Needle insertion is an established procedure for minimally invasive interventions in the liver. Up-to-date, needle insertions are precisely planned using 2D axial CT slices from 3D data sets. To provide a 3D virtual reality and haptic training and planning environment, the full segmentation of patient data is often a mandatory step. To lessen the time required for manual segmentation, we propose direct haptic volume-rendering based on CT gray values and partially segmented patient data. The core contribution is a new force output evaluation method driven by a ray-casting technique that defines paths from the skin to target structures, i.e., the right hepatic duct near the juncture with the common hepatic duct. A ray-casting method computes insertion trajectories from the skin to the duct considering no-go structures and plausibility criteria. A rating system scores each trajectory. Finally, the best insertion trajectories are selected that reach the target. Along the selected paths, force output comparison between a reference system and the new haptic force output algorithm is carried out, quantified and visualized.


   The evaluation framework is presented along with an exemplary study of the liver using the atlas data set from a reference patient. In a comparison of our reference method to a newer algorithm, force outputs are found to be similar in 99 % of the paths.


   The proposed evaluation framework allows reliable detection of problematic PTCD trajectories and provides valuable hints to improve force feedback algorithm development.


Training Planning Needle puncture  Haptic force feedback PTCD Evaluation framework 



This work is supported by the German Research Foundation (DFG, HA 2355/10-1).

Conflict of interest

Andre Mastmeyer has no conflict of interest. Tobias Hecht has no conflict of interest. Dirk Fortmeier has no conflict of interest. Heinz Handels has no conflict of interest.

Informed consent Informed consent was obtained from all patients for being included in the study. The identity of the subjects under study is not revealed.


  1. 1.
    Abolhassani N, Patel R, Moallem M (2007) Needle insertion into soft tissue: a survey. Med Eng Phys 29(4):413–431PubMedCrossRefGoogle Scholar
  2. 2.
    Baegert C, Villard C, Schreck P, Soler L (2007) Multi-criteria trajectory planning for hepatic radiofrequency ablation. MICCAI Med Image Comput Comput Interv 4792:676–684Google Scholar
  3. 3.
    Basdogan C, Sedef M, Harders M, Wesarg S (2007) VR-based simulators for training in minimally invasive surgery. IEEE Comput Graph Appl 27(2):54–66PubMedCrossRefGoogle Scholar
  4. 4.
    Bresenham JE (1965) Algorithm for computer control of a digital plotter. IBM Syst J 4(1):25–30CrossRefGoogle Scholar
  5. 5.
    Campadelli P, Casiraghi E, Esposito A (2009) Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med 45(2–3):185–196PubMedCrossRefGoogle Scholar
  6. 6.
    Dierckx P, Suetens P, Vandermeulen D (1988) An algorithm for surface reconstruction from planar contours using smoothing splines. J Comput Appl Math 23(3):367–388CrossRefGoogle Scholar
  7. 7.
    Engel K (2006) Real-time volume graphics. Ak Peters Series. AK Peters, LimitedGoogle Scholar
  8. 8.
    Färber M, Hoeborn E, Dalek D, Hummel F, Gerloff C, Bohn CA, Handels H (2008) Training and evaluation of lumbar punctures in a VR-environment using a 6DOF haptic device. MMVR16/Stud Health Technol. Inform 132:112–114Google Scholar
  9. 9.
    Färber M, Hummel F, Gerloff C, Handels H (2009) Virtual reality simulator for the training of lumbar punctures. Methods Inf Med 48(5):493–501PubMedCrossRefGoogle Scholar
  10. 10.
    Heimann T, van Ginneken B, Styner M, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman P, Chi Y, Cordova A, Dawant B, Fidrich M, Furst J, Furukawa D, Grenacher L, Hornegger J, Kainmuller D, Kitney R, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu D, Rau AM, van Rikxoort E, Rousson M, Rusko L, Saddi K, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite J, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. Trans Med Imaging 28(8):1251–1265 Google Scholar
  11. 11.
    Liwu L (1997) Practical clinical ultrasound diagnosis. World Scientific Publishing CompanyGoogle Scholar
  12. 12.
    Lundin K, Ynnerman A, Gudmundsson B (2002) Proxy-based haptic feedback from volumetric density data. Eurohaptics Conference pp 104–109Google Scholar
  13. 13.
    Mastmeyer A, Fortmeier D, Handels H (2012) Direct haptic volume rendering in lumbar puncture simulation. Stud Health Technol Inform 173:280–286PubMedGoogle Scholar
  14. 14.
    Mastmeyer A, Fortmeier D, Handels H (2012) Anisotropic diffusion for direct haptic volume rendering in lumbar puncture simulation. In: Tolxdorff T, Deserno TM, Handels H, Meinzer HP (Hrsg.), Bildverarbeitung für die Medizin, (2012) Informatik aktuell. Springer Verlag, Berlin, pp 286–291Google Scholar
  15. 15.
    Mastmeyer A, Fortmeier D, Maghsoudi E, Simon M, Handels H (2013) Patch-based label fusion using local confidence-measures and weak segmentations. SPIE Medical Imaging 2013Google Scholar
  16. 16.
    Mastmeyer A, Hecht T, Fortmeier D, Handels H (2013) Raycasting based evaluation framework for needle insertion force feedback algorithms. In: Tolxdorff T, Deserno TM, Handels H, Meinzer HP (Hrsg.), Bildverarbeitung für die Medizin, (2013) Informatik aktuell. Springer, Berlin, pp 3–8Google Scholar
  17. 17.
    Mharib AM, Ramli AR, Mashohor S, Mahmood RB (2011) Survey on liver CT image segmentation methods. Artif Intell Rev 37(2):83–95CrossRefGoogle Scholar
  18. 18.
    Murphy MJ (2004) Tracking moving organs in real time. Semin Radiat Oncol 14(1):91–100Google Scholar
  19. 19.
    Nath S, Chen Z, Yue N, Trumpore S, Peschel R (2000) Dosimetric effects of needle divergence in prostate seed implant using 125l and 103pd radioactive seeds. Med Phys 27(5):1058–1066PubMedCrossRefGoogle Scholar
  20. 20.
    Pereira PL (2007) Actual role of radiofrequency ablation of liver metastases. Eur Radiol 17(8):2062–2070PubMedCrossRefGoogle Scholar
  21. 21.
    Ruspini DC, Kolarov K, Khatib O (1997) The haptic display of complex graphical environments. In: Proceedings of the 24th annual conference on Computer graphics and interactive techniques SIGGRAPH vol 97, pp 345–352Google Scholar
  22. 22.
    Seitel A, Engel M, Sommer CM, Radeleff BA, Caroline EV, Baegert C, Fangerau M, Fritzsche KH, Yung K, Meinzer HP, Maier-Hein L (2011) Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys 38(6):3246–3259PubMedCrossRefGoogle Scholar
  23. 23.
    Ullrich S, Grottke O, Fried E, Frommen T, Liao W, Rossaint R, Kuhlen T, Deserno TM (2009) An intersubject variable regional anesthesia simulator with a virtual patient architecture. Int J Comput Assist Radiol Surg 4(6):561–570PubMedCrossRefGoogle Scholar
  24. 24.
    Ullrich S, Kuhlen T (2012) Haptic palpation for medical simulation in virtual environments. IEEE Trans Vis Comput Gr 18(4):617–625CrossRefGoogle Scholar

Copyright information

© CARS 2013

Authors and Affiliations

  • Andre Mastmeyer
    • 1
    Email author
  • Tobias Hecht
    • 1
  • Dirk Fortmeier
    • 1
    • 2
  • Heinz Handels
    • 1
  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckGermany
  2. 2.Graduate School for Computing in Medicine and Life SciencesUniversity of LübeckLübeckGermany

Personalised recommendations