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A new geometry-based plan for inserting flexible needles to reach multiple targets

Published online by Cambridge University Press:  16 December 2013

Oleg A. Bobrenkov
Affiliation:
Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Jaeyeon Lee
Affiliation:
Department of Electrical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Wooram Park*
Affiliation:
Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
*
*Corresponding author, email: wooram.park@utdallas.edu

Summary

The tip of a flexible needle with a bevel tip approximately follows a planar arc when it is inserted into soft tissue only with the force applied to the needle along the needle axis. The direction of the arc can be controlled by the rotation input around the needle axis. This flexible and steerable needle has been shown to have a considerable potential in clinical applications due to its maneuverability and steerability. Beyond the needle insertion to a single destination, this paper concerns obtaining needle trajectories that reach multiple targets. Specifically, we propose an algorithm for the insertion of a flexible needle to travel from a single insertion point (i.e. port) to multiple targets. The insertion is motivated by the observation that multiple targets can be reached by the flexible needle through a combination of insertion, partial retraction, turning, and reinsertion of the flexible needle. In this paper we develop an insertion algorithm that minimizes tissue damage during the needle insertion to multiple targets. To this end, a cost function which computes the length of needle trajectory that can be thought of as the tissue damage is defined, and is minimized. Through the minimization, we find the optimal insertion parameters such as the port location, the insertion direction at the port, the targeting order, the turning angles, and the lengths of forward insertions and retractions. To reduce the computation time, we perform workspace analysis for this approach to filter out the no-solution cases. We present numerical examples of the simulated needle insertion for multiple targets with and without obstacles and show the benefit of the proposed method in terms of the tissue damage and the number of skin punctures. Extensions of the proposed approach to more complex cases such as more than three target points and maneuvering around spherical obstacles are also discussed.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

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References

1. Abolhassani, N., Patel, R. and Moallem, M., “Needle insertion into soft tissue: A survey,” Med. Eng. Phys. 29 (4), 413431 (2007).CrossRefGoogle ScholarPubMed
2. Alterovitz, R., Branicky, M. and Goldberg, K., “Motion planning under uncertainty for image-guided medical needle steering,” Int. J. Robot. Res. 27 (11–12), 13611374 (2008).Google Scholar
3. Alterovitz, R., Goldberg, K. and Okamura, A., “Planning for Steerable Bevel-Tip Needle Insertion Through 2D Soft Tissue with Obstacles,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2005), pp. 1640–1645.Google Scholar
4. Bishoff, J. T., Stoianovici, D., Lee, B. R., Bauer, J., Taylor, R. H., Whitcomb, L. L., Cadeddu, J. A., Chan, D. and Kavoussi, L. R., “RCM-PAKY: Clinical application of a new robotic system for precise needle placement,” J. Endourol. 12 (82) (1998), S82.Google Scholar
5. Bradski, G. and Kaehler, A., Learning OpenCV: Computer Vision with the OpenCV Library (O'Reilly Media, Newton, MA, 2008).Google Scholar
6. Chen, A. and Vijayakumar, S., Prostate Cancer (Demos Medical, New York, NY, 2011).Google Scholar
7. Chentanez, N., Alterovitz, R., Ritchie, D., Cho, L., Hauser, K. K., Goldberg, K., Shewchuk, J. R. and O'Brien, J. F., “Interactive simulation of surgical needle insertion and steering,” ACM Trans. Graph. (TOG) 28 (3), 88 (2009).Google Scholar
8. DiMaio, S. P. and Salcudean, S. E., “Needle steering and motion planning in soft tissues,” IEEE Trans. Biomed. Eng. 52 (6), 965974 (2005).Google Scholar
9. Dubins, L. E., “On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents,” Am. J. Math. 79, 497516 (1957).Google Scholar
10. Duindam, V., Alterovitz, R., Sastry, S. and Goldberg, K., “Screw-Based Motion Planning for Bevel-Tip Flexible Needles in 3D Environments with Obstacles,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2008) pp. 2483–2488.Google Scholar
11. Duindam, V., Xu, J., Alterovitz, R., Sastry, S. and Goldberg, K., “Three-dimensional motion planning algorithms for steerable needles using inverse kinematics,” Int. J. Robot. Res. 29 (7), 789800 (2010).Google Scholar
12. Glozman, D. and Shoham, M., “Image-guided robotic flexible needle steering,” IEEE Trans. Robot. 23 (3), 459467 (2007).Google Scholar
13. Hauser, K., Alterovitz, R., Chentanez, N., Okamura, A. and Goldberg, K., “Feedback Control for Steering Needles Through 3D Deformable Tissue Using Helical Paths,” Robotics Science and Systems: Online Proceedings. Available at: http://www.roboticsproceedings.org/rss05/p37.pdf (2009) (accessed Dec. 2, 2011) 37 p.Google Scholar
14. Lobaton, E. J., Zhang, J., Patil, S. and R. Alterovitz, “Planning Curvature-Constrained Paths to Multiple Goals Using Circle Sampling,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2011) pp. 1463–1469.Google Scholar
15. Majewicz, A., Wedlick, T. R., Reed, K. B. and Okamura, A. M., “Evaluation of Robotic Needle Steering in ex vivo Tissue,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2010) pp. 2068–2073.Google Scholar
16. Mallapragada, V., Sarkar, N. and Podder, T. K., “Robot-Assisted Real-Time Tumor Manipulation for Breast Biopsy,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2008) pp. 2515–2520.Google Scholar
17. Misra, S., Reed, K. B., Schafer, B. W., Ramesh, K. T. and Okamura, A. M., “Mechanics of flexible needles robotically steered through soft tissue,” Int. J. Robot. Res. 29 (13), 1640 (2010).Google Scholar
18. National Institutes of Health (NIH), Liver biopsy. Available at: http://www.digestive.niddk.nih.gov/ddiseases/pubs/liverbiopsy/ (accessed Dec. 2, 2011).Google Scholar
19. Okazawa, S., Ebrahimi, R., Chuang, J., Salcudean, S. E. and Rohling, R., “Hand-held steerable needle device,” IEEE/ASME Trans. Mechatronics 10 (3), 285296 (2005).Google Scholar
20. Park, W., Kim, J. S., Zhou, Y., Cowan, N. J., Okamura, A. M. and Chirikjian, G. S., “Diffusion-based Motion Planning for a Nonholonomic Flexible Needle Model,” Proceedings of IEEE International Conference on Robotics and Automation(ICRA) (2005) pp. 4600–4605.Google Scholar
21. Park, W., Liu, Y., Zhou, Y., Moses, M. and Chirikjian, G. S., “Kinematic state estimation and motion planning for stochastic nonholonomic systems using the exponential map,” Robotica 26 (4), 419434 (2008).Google Scholar
22. Park, W., Reed, K. B., Okamura, A. M. and Chirikjian, G. S., “Estimation of Model Parameters for Steerable Needles,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2010) pp. 3703–3708.Google Scholar
23. Park, W., Wang, Y. and Chirikjian, G. S., “The path-of-probability algorithm for steering and feedback control of flexible needles,” Int. J. Robot. Res. 29 (7), 813 (2010).Google Scholar
24. Reed, K. B., Majewicz, A., Kallem, V., Alterovitz, R., Goldberg, K., Cowan, N. J. and Okamura, A. M., “Robot-assisted needle steering,” Robot. Autom. Mag. IEEE 18 (4), 3546 (2011).Google Scholar
25. Reed, K. B., Okamura, A. M. and Cowan, N. J., “Modeling and control of needles with torsional friction,” IEEE Trans. Biomed. Eng. 56 (12), 29052916 (2009).Google Scholar
26. Reed, K. B., Kallem, V., Alterovitz, R., Goldberg, K., Okamura, A. M. and Cowan, N. J., “Integrated Planning and Image-Guided Control for Planar Needle Steering,” Proceedings of IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) (2008) pp. 819–824.Google Scholar
27. Rizun, P. R., McBeth, P. B., Louw, D. F. and Sutherland, G. R., “Robot-assisted neurosurgery,” Semin. Laparosc. Surg. 11 (2), 99106 (2004).Google Scholar
28. Szeliski, R., Computer Vision: Algorithms and Applications (Springer, New York, NY, 2010).Google Scholar
29. Torabi, M., Hauser, K., Alterovitz, R., Duindam, V. and Goldberg, K., “Guiding Medical Needles Using Single-Point Tissue Manipulation,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2009), pp. 2705–2710.Google Scholar
30. Webster, R. J. III, Memisevic, J. and Okamura, A. M., “Design Considerations for Robotic Needle Steering,” Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2005) pp. 3588–3594.Google Scholar
31. Webster, R. J. III, Kim, J. S., Cowan, N. J., Chirikjian, G. S. and Okamura, A. M., “Nonholonomic modeling of needle steering,” Int. J. Robot. Res. 25 (5–6), 509525 (2006).CrossRefGoogle Scholar
32. Wei, Z., Wan, G., Gardi, L., Mills, G., Downey, D. and A. Fenster, “Robot-assisted 3D-TRUS-guided prostate brachytherapy: System integration and validation,” Med. Phys. 31 (3), 539548 (2004).Google Scholar
33. Xu, J., Duindam, V., Alterovitz, R., Pouliot, J., Cunha, J. A. M., Hsu, I. C. and Goldberg, K., “Planning “Fireworks” Trajectories for Steerable Medical Needles to Reduce Patient Trauma,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009) pp. 4517–4522.Google Scholar
34. Zivanovic, A. and Davies, B. L., “A robotic system for blood sampling,” IEEE Trans. Inform. Technol. Biomed. 4 (1), 814 (2000).Google Scholar