Journal of Medical and Biological Engineering

, Volume 38, Issue 5, pp 697–706 | Cite as

Mechanical-Based Model for Extra-Fine Needle Tip Deflection Until Breaching of Tissue Surface

  • Ryosuke Tsumura
  • Tomoyuki Miyashita
  • Hiroyasu Iwata
Original Article


Accurate estimation of needle deflection is necessary to successfully steer the needle to targets located deep inside the body. In particular, the deflection that occurs until the tissue surface is breached differs according to the tissue shape and stiffness. This topic has not been a focus of previous work. In the present paper, we propose a model with which to estimate the needle deflection that occurs until breaching of the tissue surface with consideration of the tissue shape and stiffness. This model comprises a cantilever beam supported by virtual springs that represent the interaction forces between the needle tip and tissue surface. The effects of different insertion angles and tissue stiffness on needle deflection are represented by changing the spring constants. The model was used in experiments involving four different insertion angles (0º, 15º, 30º, and 45º) and three different polyvinyl chloride (PVC) phantoms with different stiffness (100, 75, and 50%). We verified the proposed model with the 80% PVC phantom and showed a maximum error of 0.04 mm.


Needle steering Deflection estimation Rayleigh–Ritz method Mechanical-based model 


  1. 1.
    Moreira, P., & Misra, S. (2014). Biomechanics-based curvature estimation for ultrasound-guided flexible needle steering in biological tissues. Annals of Biomedical Engineering. Scholar
  2. 2.
    Majewicz, A., Marra, S., van Vledder, M., Lin, M., Choti, M., Song, D., et al. (2012). Behavior of tip-steerable needles in ex vivo and in vivo tissue. IEEE Transactions on Biomedical Engineering, 59, 2705–2715.CrossRefGoogle Scholar
  3. 3.
    Abayazid, M., Roesthuis, R. J., Reilink, R., & Misra, S. (2013). Integrating deflection models and image feedback for real-time flexible needle steering. IEEE Transactions on Robotics, 29, 542–553.CrossRefGoogle Scholar
  4. 4.
    Vrooijink, G., & Abayazid, M. (2014). Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images. International Journal of Robotics Research, 33, 1361–1374.CrossRefGoogle Scholar
  5. 5.
    Abolhassani, N., Patel, R., & Moallem, M. (2007). Needle insertion into soft tissue: A survey. Medical Engineering and Physics, 29, 413–431.CrossRefGoogle Scholar
  6. 6.
    Okamura, A. M., Simone, C., & O’Leary, M. D. (2004). Force modeling for needle insertion into soft tissue. IEEE Transactions on Biomedical Engineering, 51, 1707–1716.CrossRefGoogle Scholar
  7. 7.
    Asadian, A., Patel, R. V., & Kermani, M. R. (2014). Dynamics of translational friction in needle-tissue interaction during needle insertion. Annal of Biomedical Engineering, 42, 73–85.CrossRefGoogle Scholar
  8. 8.
    Khadem, M., Rossa, C., Sloboda, R. S., Usmani, N., & Tavakoli, M. (2016). Mechanics of tissue cutting during needle insertion in biological tissue. IEEE Robotics and Automation Letter, 1, 800–807.CrossRefGoogle Scholar
  9. 9.
    Barbé, L., Bayle, B., De Mathelin, M., & Gangi, A. (2007). Needle insertions modelling: Identifiability and limitations. Biomedical Signal Processing and Control, 2, 191–198.CrossRefGoogle Scholar
  10. 10.
    van Gerwen, D. J., Dankelman, J., & van den Dobbelsteen, J. J. (2012). Needle–tissue interaction forces: A survey of experimental data. Medical Engineering and Physics, 34, 665–680.CrossRefGoogle Scholar
  11. 11.
    Webster, R. J., III, Cowan, N. J., Chirikjian, G. S., & Okamura, M. (2006). Nonholonomic modelling of needle steering. International Journal of Robotics Research, 25, 509–525.CrossRefGoogle Scholar
  12. 12.
    Yan, K., Ng, W., Ling, K., Yu, Y., Podder, T., Liu, T., & Cheng C. (2006). Needle steering modeling and analysis using unconstrained modal analysis. Proceedings of First IEEE/RAS-EMBS International Conference Biomedical Robotics and Biomechatronics (BioRob).
  13. 13.
    Khadem, M., Fallahi, B., Rossa, C., Sloboda, R., Usmani, N., & Tavakoli, M. (2015). A mechanics-based model for simulation and control of flexible needle insertion in soft tissue. Proceedings of IEEE International Conference on Robotics and Automation (pp. 2264–2269).
  14. 14.
    Misra, S., Reed, K. B., Schafer, B. W., Ramesh, K. T., & Okamura, M. (2010). Mechanics of flexible needles robotically steered through soft tissue. International Journal of Robotics Research, 29, 1640–1660.CrossRefGoogle Scholar
  15. 15.
    DiMaio, S. P., & Salcudean, S. E. (2005). Interactive simulation of needle insertion models. IEEE Transactions on Biomedical Engineering, 52, 1167–1179.CrossRefGoogle Scholar
  16. 16.
    Goksel, O., Dehghan, E., & Salcudean, S. E. (2009). Modeling and simulation of flexible needles. Medical Engineering and Physics, 31, 1069–1078.CrossRefGoogle Scholar
  17. 17.
    Alterovitz, R., Goldberg, K., & Okamura, A. (2005). Planning for steerable bevel-tip needle insertion through 2D soft tissue with obstacles. Proceedings of IEEE International Conference on Robotics and Automation (pp. 1640–1645).
  18. 18.
    Yamaguchi, S., Tsutsui, K., Satake, K., Morikawa, S., Shirai, Y., & Tanaka, H. (2014). Dynamic analysis of a needle insertion for soft materials: Arbitrary Lagrangian–Eulerian-based three-dimensional finite element analysis. Computers in Biology and Medicine, 53, 42–47.CrossRefGoogle Scholar
  19. 19.
    Rossa, C., Khadem, M., Sloboda, R., Usmani, N., & Tavakoli, M. (2016). Adaptive quasi-static modelling of needle deflection during steering in soft tissue. IEEE Robotics and Automation Letter, 1, 916–923.CrossRefGoogle Scholar
  20. 20.
    Assaad, W., Jahya, A., Moreira, P., & Misra, S. (2015). Finite-element modeling of a bevel-tipped needle interacting with gel. Journal of Mechanics in Medicine and Biology, 15, 1550079-1–1550079-15.CrossRefGoogle Scholar
  21. 21.
    Barnett, A. C., Lee, Y.-S., & Moore, J. Z. (2015). Fracture mechanics model of needle cutting tissue. Journal of Manufacturing Science and Engineering, 138, 011005-1–011005-8.CrossRefGoogle Scholar
  22. 22.
    Misra, S., Macura, K. J., Ramesh, K. T., & Okamura, A. M. (2009). The importance of organ geometry and boundary constraints for planning of medical interventions. Medical Engineering and Physics, 31, 195–206.CrossRefGoogle Scholar
  23. 23.
    Jahya, A., Schouten, M. G., Fütterer, J. J., & Misra, S. (2012). On the importance of modelling organ geometry and boundary conditions for predicting three-dimensional prostate deformation. Computer Methods in Biomechanics and Biomedical Engineering. Scholar
  24. 24.
    Roesthuis, R. J., van Veen, Y. R. J., Jahya, A., & Misra, S. (2011). Mechanics of needle-tissue interaction. Proceedings of IEEE/RSJ International Conference Intelligent Robots and Systems (pp. 2557–2563).
  25. 25.
    Lee, H., & Kim, J. (2014). Estimation of flexible needle deflection in layered soft tissues with different elastic moduli. Medical and Biological Engineering and Computing, 52, 729–740. Scholar
  26. 26.
    Mahvash, M., & Dupont, P. E. (2009). Fast needle insertion to minimize tissue deformation and damage. Proceedings of IEEE International Conference on Robotics and Automation (pp. 3097–3102).
  27. 27.
    Elgezua, I., Kobayashi, Y., & Fujie, M. G. (2014). Estimation of needle tissue interaction based on non-linear elastic modulus and friction force patterns. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4315–4320).

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  1. 1.Graduate School of Creative Science and EngineeringWaseda UniversityTokyoJapan
  2. 2.Faculty of Science and EngineeringWaseda UniversityTokyoJapan

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