Annals of Biomedical Engineering

, Volume 45, Issue 4, pp 924–938 | Cite as

Semi-Automated Needle Steering in Biological Tissue Using an Ultrasound-Based Deflection Predictor

  • Mohsen Khadem
  • Carlos Rossa
  • Nawaid Usmani
  • Ron S. Sloboda
  • Mahdi Tavakoli


The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm.


Medical robotics Needle steering Motion planning Homotopy analysis method 



This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under grant CHRP 446520, the Canadian Institutes of Health Research (CIHR) under grant CPG 127768 and the Alberta Innovates - Health Solutions (AIHS) under grant CRIO 201201232. The authors would like to thank Dr. Muhammad Faisal Jamaluddin who worked closely with us in conducting the evaluation experiments and helping to analyze our research.


  1. 1.
    Adebar, T. K., A. E. Fletcher, and A. M. Okamura. 3-D ultrasound-guided robotic needle steering in biological tissue. IEEE Tran. Biomed. Eng. 61:2899–2910, 2014.CrossRefGoogle Scholar
  2. 2.
    Choi, A. P. C., and Y. P. Zheng. Estimation of Young’s modulus and Poisson’s ratio of soft tissue from indentation using two different-sized indentors: Finite element analysis of the finite deformation effect. Med. Biol. Eng. Comput. 43:258–264, 2005.CrossRefPubMedGoogle Scholar
  3. 3.
    Cowan, N. J., K. Goldberg, G. S. Chirikjian, G. Fichtinger, R. Alterovitz, K. B. Reed, V. Kallem, W. Park, S. Misra, and A. M. Okamura. Surgical Robotics: Systems Applications and Visions. US: Springer, pp. 557–582, 2011.CrossRefGoogle Scholar
  4. 4.
    Delling, D., P. Sanders, D. Schultes, and D. Wagner. Algorithmics of Large and Complex Networks: Design, Analysis, and Simulation. Berlin: Springer, pp. 117–139, 2009.CrossRefGoogle Scholar
  5. 5.
    Goksel, O., E. Dehghan, and S. E. Salcudean. Modeling and simulation of flexible needles. Med. Eng. Phys. 31:1069–1078, 2009.CrossRefPubMedGoogle Scholar
  6. 6.
    Jamaluddin, M. F., S. Ghosh, M. Waine, et al. Quantifying iodine-125 placement accuracy in prostate brachytherapy using post-implant transrectal ultrasound images. Brachytherapy 15:S180, 2016.CrossRefGoogle Scholar
  7. 7.
    Khadem, M., C. Rossa, R. S. Sloboda, N. Usmani, and M. Tavakoli. Ultrasound-guided model predictive control of needle steering in biological tissue. J. Med. Robot. Res 01:1640007–1640007, 2016.CrossRefGoogle Scholar
  8. 8.
    Khadem, M., C. Rossa, N. Usmani, R. S. Sloboda, and M. Tavakoli. A two-body rigid/flexible model of needle steering dynamics in soft tissue. IEEE/ASME Trans Mechatron. 21:2352–2364, 2016.CrossRefGoogle Scholar
  9. 9.
    Liao, S. Homotopy analysis method: a new analytic method for nonlinear problems. Appl. Math. Mech. 19:957–962, 1998.CrossRefGoogle Scholar
  10. 10.
    Liao, S. Homotopy Analysis Method in Nonlinear Differential Equations. Berlin: Springer, 2012.CrossRefGoogle Scholar
  11. 11.
    Maghsoudi, A., and M. Jahed. Needle dynamics modelling and control in prostate brachytherapy. IET Control Theory Appl. 6:1671–1681, 2012.CrossRefGoogle Scholar
  12. 12.
    Minhas D. S., J. A. Engh, M. M. Fenske, and C. N. Riviere. Modeling of needle steering via duty-cycled spinning. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 2756–2759Google Scholar
  13. 13.
    Misra S., K. B. Reed, A. S. Douglas, K. T. Ramesh, and A. M. Okamura. Needle-tissue interaction forces for bevel-tip steerable needles. In: 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob, 2008, pp. 224–231Google Scholar
  14. 14.
    Misra, S., K. B. Reed, B. W. Schafer, K. T. Ramesh, and A. M. Okamura. Mechanics of flexible needles robotically steered through soft tissue. Int. J. Robot. Res. 29:1640–1660, 2010.CrossRefGoogle Scholar
  15. 15.
    Moreira, P., and S. Misra. Biomechanics-based curvature estimation for ultrasound-guided flexible needle steering in biological tissues. Ann. Biomed. Eng. 43:1716–1726, 2015.CrossRefPubMedGoogle Scholar
  16. 16.
    Patil, S., J. Burgner, R. J. Webster, and R. Alterovitz. Needle steering in 3D via rapid replanning. IEEE Trans. Robot. 30:853–864, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Podder T. K., D. P. Clark, D. Fuller, J. Sherman and Effects of velocity modulation during surgical needle insertion. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS. pp. 5766–5770Google Scholar
  18. 18.
    Reed K. B., V. Kallem, R. Alterovitz, K. Goldberg, A. M. Okamura, and N. J. Cowan. Integrated planning and image-guided control for planar needle steering. In: 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob, pp. 819–824Google Scholar
  19. 19.
    Roesthuis R. J., M. Abayazid and S. Misra. Mechanics-based model for predicting in-plane needle deflection with multiple bends. In: 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 69–74Google Scholar
  20. 20.
    Rossa C., N. Usmani, R. Sloboda and M. Tavakoli. A hand-held assistant for semi-automated percutaneous needle steering. IEEE Trans. Biomed. Eng. pp. 1–1, 2016Google Scholar
  21. 21.
    Rucker, D. C., J. Das, H. B. Gilbert, P. J. Swaney, M. I. Miga, N. Sarkar, and R. J. Webster. Sliding mode control of steerable needles. IEEE Trans. Robot. 29:1289–1299, 2013.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Swensen, J. P., M. Lin, A. M. Okamura, and N. J. Cowan. Torsional dynamics of steerable needles: modeling and fluoroscopic guidance. IEEE Trans. Biomed. Eng. 61:2707–2717, 2014.CrossRefPubMedGoogle Scholar
  23. 23.
    Vrooijink G. J., M. Abayazid, S. Patil, R. Alterovitz, and S. Misra. Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images. Int. J. Robot. Res., 2014Google Scholar
  24. 24.
    Waine M., C. Rossa, R. Sloboda, N. Usmani and M. Tavakoli. 3D needle shape estimation in TRUS-guided prostate brachytherapy using 2D ultrasound images. IEEE J. Biomed. Health Inf., pp. 1–1, 2015Google Scholar
  25. 25.
    Webster R., N. Cowan, G. Chirikjian, and A. Okamura. Nonholonomic modeling of needle steering. In: Experimental Robotics, Vol. IX. Berlin: Springer, 2006, pp. 35–44Google Scholar
  26. 26.
    Yan, K. G., T. Podder, Y. Yu, T. I. Liu, C. W. S. Cheng, and W. S. Ng. Flexible needle-tissue interaction modeling with depth-varying mean parameter: preliminary study. IEEE Trans. Biomed. Eng. 56:255–262, 2009.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.The Cross Cancer Institute and the Department of OncologyUniversity of AlbertaEdmontonCanada

Personalised recommendations