Advertisement

Robotic Needle Steering: Design, Modeling, Planning, and Image Guidance

  • Noah J. Cowan
  • Ken Goldberg
  • Gregory S. Chirikjian
  • Gabor Fichtinger
  • Ron Alterovitz
  • Kyle B. Reed
  • Vinutha Kallem
  • Wooram Park
  • Sarthak Misra
  • Allison M. Okamura
Chapter

Abstract

This chapter describes how advances in needle design, modeling, planning, and image guidance make it possible to steer flexible needles from outside the body to reach specified anatomical targets not accessible using traditional needle insertion methods. Steering can be achieved using a variety of mechanisms, including tip-based steering, lateral manipulation, and applying forces to the tissue as the needle is inserted. Models of these steering mechanisms can predict needle trajectory based on steering commands, motivating new preoperative path planning algorithms. These planning algorithms can be integrated with emerging needle imaging technology to achieve intraoperative closed-loop guidance and control of steerable needles.

Keywords

Needle Insertion Tissue Deformation Needle Steering Prostate Brachytherapy Needle Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors thank Dr. Purang Abolmaesumi and Meysam Torabi for their detailed feedback on this chapter. This work was supported in part by the National Institutes of Health under Grants R21-EB003452, R01-EB006435, and F32-CA124138.

References

  1. 1.
    Abolhassani, N., Patel, R.V., Ayazi, F.: Minimization of needle deflection in robot-assisted percutaneous therapy. Int. J. Med. Robot. and Comp. Assist. Surg. 3, 140–148 (2007)CrossRefGoogle Scholar
  2. 2.
    Aboofazeli, M., Abolmaesumi, P., Mousavi, P., Fichtinger, G.: A new scheme for curved needle segmentation in three-dimensional ultrasound images. In: Proc. IEEE Int. Symp. on Biomedical Imaging, pp. 1067–1070. Boston, MA (2009)Google Scholar
  3. 3.
    Alterovitz, R., Branicky, M., Goldberg, K.: Constant-curvature motion planning under uncertainty with applications in image-guided medical needle steering. In: Akella, S., Amato, N.M., Huang, W.H., Mishra, B. (eds.) Algorithmic Foundations of Robotics. Springer Tracts in Advanced Robotics, vol. 47, pp. 319–334. Springer, Berlin (2008)Google Scholar
  4. 4.
    Alterovitz, R., Branicky, M., Goldberg, K.: Motion planning under uncertainty for image-guided medical needle steering. Int. J. Robot. Res. 27(11–12), 1361–1374 (2008)CrossRefGoogle Scholar
  5. 5.
    Alterovitz, R., Goldberg, K.: Motion Planning in Medicine: Optimization and Simulation Algorithms for Image-Guided Procedures. Springer Tracts in Advanced Robotics, vol. 50. Springer, Berlin (2008)Google Scholar
  6. 6.
    Alterovitz, R., Goldberg, K., Okamura, A.M.: Planning for steerable bevel-tip needle insertion through 2D soft tissue with obstacles. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 1652–1657. Barcelona, Spain (2005)Google Scholar
  7. 7.
    Alterovitz, R., Goldberg, K.Y., Pouliot, J., Hsu, I.C.: Sensorless motion planning for medical needle insertion in deformable tissues. IEEE Trans. Inf. Technol. Biomed. 13(2), 217–225 (2009)CrossRefGoogle Scholar
  8. 8.
    Alterovitz, R., Lim, A., Goldberg, K., Chirikjian, G.S., Okamura, A.M.: Steering flexible needles under Markov motion uncertainty. In: Proc. IEEE/RSJ Int. Conf. on Intell. Robots and Syst., pp. 1570–1575 (2005)Google Scholar
  9. 9.
    Alterovitz, R., Pouliot, J., Taschereau, R., Hsu, I.C., Goldberg, K.: Needle insertion and radioactive seed implantation in human tissues: Simulation and sensitivity analysis. In: Proc. IEEE Int. Conf. Robot. and Autom., vol. 2, pp. 1793–1799. Taipei, Taiwan (2003)Google Scholar
  10. 10.
    Alterovitz, R., Pouliot, J., Taschereau, R., Hsu, I.C., Goldberg, K.: Simulating needle insertion and radioactive seed implantation for prostate brachytherapy. In: Westwood, J.D., Hoffman, H.M., Mogel, G.T., Phillips, R., Robb, R.A.,  Stredney, D. (eds.) Medicine Meets Virtual Reality, pp. 19–25. IOS Press, Newport Beach, CA (2003)Google Scholar
  11. 11.
    Alterovitz, R., Siméon, T., Goldberg, K.: The Stochastic Motion Roadmap: A sampling framework for planning with Markov motion uncertainty. In: Burgard, W., Brock, O., Stachniss, C. (eds.) Proc. Robotics: Science and Systems, pp. 246–253. MIT Press, Cambridge, MA (2008)Google Scholar
  12. 12.
    Bogdanich, W.: At V.A. hospital, a rogue cancer unit. The New York Times (2009)Google Scholar
  13. 13.
    Chentanez, N., Alterovitz, R., Ritchie, D., Cho, L., Hauser, K.K., Goldberg, K., Shewchuk, J.R., O’Brien, J.F.: Interactive simulation of surgical needle insertion and steering. ACM Transactions on Graphics (Proc. SIGGRAPH). 28(3), 88:1–10 (2009)Google Scholar
  14. 14.
    Cheung, S., Rohling, R.: Enhancement of needle visibility in ultrasound-guided percutaneous procedures. Ultrasound Med. Biol. 30(5), 617–624 (2004)CrossRefGoogle Scholar
  15. 15.
    Crouch, J.R., Schneider, C.M., Wainer, J., Okamura, A.M.: A velocity-dependent model for needle insertion in soft tissue. In: Medical Image Computing and Computer Assisted Intervention. Lecture Notes in Computer Science, vol. 3750, pp. 624–632. Springer, Berlin (2005)Google Scholar
  16. 16.
    DiMaio, S.P., Kacher, D.F., Ellis, R.E., Fichtinger, G., Hata, N., Zientara, G.P., Panych, L.P., Kikinis, R., Jolesz, F.A.: Needle artifact localization in 3T MR images. Stud. Health Technol. Inform. 119, 120–125 (2006)Google Scholar
  17. 17.
    DiMaio, S.P., Salcudean, S.E.: Needle insertion modeling and simulation. IEEE Trans. Robot. Autom. 19(5), 864–875 (2003)CrossRefGoogle Scholar
  18. 18.
    DiMaio, S.P., Salcudean, S.E.: Needle steering and motion planning in soft tissues. IEEE Trans. Biomed. Eng. 52(6), 965–974 (2005)CrossRefGoogle Scholar
  19. 19.
    Ding, M., Fenster, A.: A real-time biopsy needle segmentation technique using hough transform. Med. Phys. 30(8), 2222–2233 (2003)CrossRefGoogle Scholar
  20. 20.
    Ding, M., Fenster, A.: Projection-based needle segmentation in 3D ultrasound images. Comput. Aided Surg. 9(5), 193–201 (2004)CrossRefGoogle Scholar
  21. 21.
    Duindam, V., Alterovitz, R., Sastry, S., Goldberg, K.: Screw-based motion planning for bevel-tip flexible needles in 3D environments with obstacles. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 2483–2488 (2008)Google Scholar
  22. 22.
    Duindam, V., Xu, J., Alterovitz, R., Sastry, S., Goldberg, K.: Three-dimensional motion planning algorithms for steerable needles using inverse kinematics. Int. J. Robot. Res. 29(7), 789–800 (2010)CrossRefGoogle Scholar
  23. 23.
    Dupont, P.E., Lock, J.L., Itkowitz, B., Butler, E.: Design and control of concentric-tube robots. IEEE Trans. Robot. 26(2), 209–225 (2010)CrossRefGoogle Scholar
  24. 24.
    Ebert-Uphoff, I., Chirikjian, G.S.: Inverse kinematics of discretely actuated hyper-redundant manipulators using workspace densities. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 139–145 (1996)Google Scholar
  25. 25.
    Engh, J., Podnar, G., Kondziolka, D., Riviere, C.: Toward effective needle steering in brain tissue. In: Proc. IEEE Int. Conf. Eng. Med. Biol. Soc., pp. 559–562 (2006)Google Scholar
  26. 26.
    Fichtinger, G., Fiene, J., Kennedy, C.W., Kronreif, G., Iordachita, I., Song, D.Y., Burdette, E.C., Kazanzides, P.: Robotic assistance for ultrasound guided prostate brachytherapy. In: Medical Image Computing and Computer Assisted Intervention. Lecture Notes in Computer Science, pp. 119–127. Springer, Brisbane, Australia (2007)Google Scholar
  27. 27.
    Glozman, D., Shoham, M.: Image-guided robotic flexible needle steering. IEEE Trans. Robot. 23(3), 459–467 (2007)CrossRefGoogle Scholar
  28. 28.
    Harmat, A., Rohling, R.N., Salcudean, S.E.: Needle tip localization using stylet vibration. Ultrasound Med. Biol. 32(9), 1339–1348 (2006)CrossRefGoogle Scholar
  29. 29.
    Hauser, K., Alterovitz, R., Chentanez, N., Okamura, A., Goldberg, K.: Feedback control for steering needles through 3D deformable tissue using helical paths. In: Proc. Robotics: Science and Systems. Seattle, USA (2009)Google Scholar
  30. 30.
    Heverly, M., Dupont, P., Triedman, J.: Trajectory optimization for dynamic needle insertion. In: Proc. IEEE Int. Conf. Robot. and Autom., vol. 1, pp. 1646–1651. Barcelona, Spain (2005)Google Scholar
  31. 31.
    Hing, J.T., Brooks, A.D., Desai, J.P.: Reality-based needle insertion simulation for haptic feedback in prostate brachytherapy. In: Proc. IEEE Int. Conf. Robot. and Autom., vol. 1, pp. 619–624. Orlando, USA (2006)Google Scholar
  32. 32.
    Jain, A.K., Mustafa, T., Zhou, Y., Burdette, C., Chirikjian, G.S., Fichtinger, G.: Ftrac–a robust fluoroscope tracking fiducial. Med. Phys. 32(10), 3185–3198 (2005)CrossRefGoogle Scholar
  33. 33.
    Kallem, V.: Vision-based control on lie groups with application to needle steering. Ph.D. thesis, Johns Hopkins University (2008)Google Scholar
  34. 34.
    Kallem, V., Chang, D.E., Cowan, N.J.: Task-induced symmetry and reduction in kinematic systems with application to needle steering. In: Proc. IEEE/RSJ Int. Conf. on Intell. Robots and Syst., pp. 3302–3308. San Diego, CA (2007)Google Scholar
  35. 35.
    Kallem, V., Chang, D.E., Cowan, N.J.: Task-induced symmetry and reduction with application to needle steering. IEEE Trans. Automat. Contr. 55(3), 664–673 (2010)CrossRefMathSciNetGoogle Scholar
  36. 36.
    Kallem, V., Cowan, N.J.: Image-guided control of flexible bevel-tip needles. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 3015–3020. Rome, Italy (2007)Google Scholar
  37. 37.
    Kallem, V., Cowan, N.J.: Image guidance of flexible tip-steerable needles. IEEE Trans. Robot. 25, 191–196 (2009)CrossRefGoogle Scholar
  38. 38.
    Krieger, A., Susil, R.C., Ménard, C., Coleman, J.A., Fichtinger, G., Atalar, E., Whitcomb, L.L.: Design of a novel MRI compatible manipulator for image guided prostate interventions. IEEE Trans. Biomed. Eng. 52(2), 306–313 (2005)CrossRefGoogle Scholar
  39. 39.
    Mallapragada, V.G., Sarkar, N., Podder, T.K.: Robot-assisted real-time tumor manipulation for breast biopsy. IEEE Trans. Robot. 25(2), 316–324 (2009)CrossRefGoogle Scholar
  40. 40.
    Mason, R., Burdick, J.: Trajectory planning using reachable-state density functions. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 273–280 (2002)Google Scholar
  41. 41.
    Minhas, D.S., Engh, J.A., Fenske, M.M., Riviere, C.N.: Modeling of needle steering via duty-cycled spinning. Proc. IEEE Int. Conf. Eng. Med. Biol. Soc. 2007, 2756–2759 (2007)Google Scholar
  42. 42.
    Misra, S., Ramesh, K.T., Okamura, A.M.: Modeling of tool-tissue interactions for computer-based surgical simulation: A literature review. Presence: Teleoperators & Virtual Environments 17(5), 463–491 (2008)Google Scholar
  43. 43.
    Misra, S., Reed, K.B., Douglas, A.S., Ramesh, K.T., Okamura, A.M.: Needle-tissue interaction forces for bevel-tip steerable needles. In: Proc. IEEE/RASJ Int. Conf. on Biomed. Robotics and Biomechatronics, pp. 224–231. Scottsdale, USA (2008)Google Scholar
  44. 44.
    Misra, S., Reed, K.B., Ramesh, K.T., Okamura, A.M.: Observations of needle-tissue interactions. In: Proc. IEEE Int. Conf. Eng. Med. Biol. Soc., pp. 262–265. Minneapolis, USA (2009)Google Scholar
  45. 45.
    Misra, S., Reed, K.B., Schafer, B.W., Ramesh, K.T., Okamura, A.M.: Observations and models for needle-tissue interactions. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 2687–2692. Kobe, Japan (2009)Google Scholar
  46. 46.
    Misra, S., Reed, K.B., Schafer, B.W., Ramesh, K.T., Okamura, A.M.: Mechanics of flexible needles robotically steered through soft tissue. Int. J. Robot. Res. (2010). URL http://ijr.sagepub.com/content/early/2010/06/02/0278364910369714.short?rss=1&ssource=mfc
  47. 47.
    Mozer, P.C., Partin, A.W., Stoianovici, D.: Robotic image-guided needle interventions of the prostate. Rev. Urol. 11(1), 7–15 (2009)Google Scholar
  48. 48.
    Nienhuys, H.W., van der Stappen, F.A.: A computational technique for interactive needle insertions in 3D nonlinear material. In: Proc. IEEE Int. Conf. Robot. and Autom., vol. 2, pp. 2061–2067. New Orleans, USA (2004)Google Scholar
  49. 49.
    Novotny, P.M., Stoll, J.A., Vasilyev, N.V., del Nido, P.J., Dupont, P.E., Zickler, T.E., Howe, R.D.: GPU based real-time instrument tracking with three-dimensional ultrasound. Med. Image Anal. 11(5), 458–464 (2007)CrossRefGoogle Scholar
  50. 50.
    Okamura, A.M., Simone, C., O’Leary, M.D.: Force modeling for needle insertion into soft tissue. IEEE Trans. Biomed. Eng. 51(10), 1707–1716 (2004)CrossRefGoogle Scholar
  51. 51.
    Okazawa, S., Ebrahimi, R., Chuang, J., Salcudean, S.E., Rohling, R.: Hand-held steerable needle device. IEEE ASME Trans. Mechatron. 10(3), 285–296 (2005)CrossRefGoogle Scholar
  52. 52.
    Okazawa, S.H., Ebrahimi, R., Chuang, J., Rohling, R.N., Salcudean, S.E.: Methods for segmenting curved needles in ultrasound images. Med. Image Anal. 10(3), 330–342 (2006)CrossRefGoogle Scholar
  53. 53.
    Park, W., Kim, J.S., Zhou, Y., Cowan, N.J., Okamura, A.M., Chirikjian, G.S.: Diffusion-based motion planning for a nonholonomic flexible needle model. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 4600–4605. Barcelona, Spain (2005)Google Scholar
  54. 54.
    Park, W., Liu, Y., Zhou, Y., Moses, M., Chirikjian, G.S.: Kinematic state estimation and motion planning for stochastic nonholonomic systems using the exponential map. Robotica 26, 419–434 (2008)MATHGoogle Scholar
  55. 55.
    Park, W., Wang, Y., Chirikjian, G.S.: The path-of-probability algorithm for steering and feedback control of flexible needles. Int. J. Robot. Res. 29(7), 813830 (2010)CrossRefGoogle Scholar
  56. 56.
    Park, W., Wang, Y., Chirikjian, G.S.: Path planning for flexible needles using second order error propagation. In: Chirikjian, G.S., Choset, H., Morales, M., Murphey, T. (eds.) Algorithmic Foundations of Robotics VIII. Springer Tracts in Advanced Robotics, pp. 583–598. Springer, Berlin (2010)Google Scholar
  57. 57.
    Podder, T., Clark, D., Sherman, J., Fuller, D., Messing, E., Rubens, D., Strang, J., Liao, L., Ng, W.S., Yu, Y.: In vivo motion and force measurement of surgical needle intervention during prostate brachytherapy. Med. Phys. 33(8), 2915–2922 (2006)CrossRefGoogle Scholar
  58. 58.
    Reed, K.B.: Compensating for torsion windup in steerable needles. In: Proc. IEEE/RASJ Int. Conf. on Biomed. Robotics and Biomechatronics, pp. 936–941. Scottsdale, AR, USA (2008)Google Scholar
  59. 59.
    Reed, K.B., Kallem, V., Alterovitz, R., Goldberg, K., Okamura, A.M., Cowan, N.J.: Integrated planning and image-guided control for planar needle-steering. In: Proc. IEEE/RASJ Int. Conf. on Biomed. Robotics and Biomechatronics, pp. 819–824. Scottsdale, AR, USA (2008)Google Scholar
  60. 60.
    Reed, K.B., Okamura, A.M., Cowan, N.J.: Modeling and control of needles with torsional friction. IEEE Trans. Biomed. Eng. 56(12), 2905–2916 (2009)CrossRefGoogle Scholar
  61. 61.
    Rucker, D.C., Webster, R.J. III, Chirikjian, G.S., Cowan, N.J.: Equilibrium conformations of concentric-tube continuum robots. Int. J. Robot. Res. (2010). In press (published online April 1, 2010)Google Scholar
  62. 62.
    Sears, P., Dupont, P.: A steerable needle technology using curved concentric tubes. In: Proc. IEEE/RSJ Int. Conf. on Intell. Robots and Syst., pp. 2850–2856 (2006)Google Scholar
  63. 63.
    Sitzman, B.T., Uncles, D.R.: The effects of needle type, gauge, and tip bend on spinal needle deflection. Anesth. Analg. 82(2), 297–301 (1996)CrossRefGoogle Scholar
  64. 64.
    Susil, R.C., Ménard, C., Krieger, A., Coleman, J.A., Camphausen, K., Choyke, P., Fichtinger, G., Whitcomb, L.L., Coleman, C.N., Atalar, E.: Transrectal prostate biopsy and fiducial marker placement in a standard 1.5T magnetic resonance imaging scanner. J. Urol. 175(1), 113–120 (2006)Google Scholar
  65. 65.
    Taschereau, R., Pouliot, J., Roy, J., Tremblay, D.: Seed misplacement and stabilizing needles in transperineal permanent prostate implants. Radiother. Oncol. 55(1), 59–63 (2000)CrossRefGoogle Scholar
  66. 66.
    Torabi, M., Hauser, K., Alterovitz, R., Duindam, V., Goldberg, K.: Guiding medical needles using single-point tissue manipulation. In: Proc. IEEE Int. Conf. Robot. and Autom., pp. 2705–2710. Kobe, Japan (2009)Google Scholar
  67. 67.
    Wang, Y., Chirikjian, G.S.: Error propagation on the Euclidean group with applications to manipulator kinematics. IEEE Trans. Robot. 22(4), 591–602 (2006)CrossRefGoogle Scholar
  68. 68.
    Wang, Y., Chirikjian, G.S.: Nonparametric second-order theory of error propagation on motion groups. Int. J. Robot. Res. 27, 1258–1273 (2008)CrossRefGoogle Scholar
  69. 69.
    Webster, R.J. III, Kim, J.S., Cowan, N.J., Chirikjian, G.S., Okamura, A.M.: Nonholonomic modeling of needle steering. Int. J. Robot. Res. 25(5–6), 509–525 (2006)CrossRefGoogle Scholar
  70. 70.
    Webster, R.J. III, Memisevic, J., Okamura, A.M.: Design considerations for robotic needle steering. In: Proc. IEEE Int. Conf. Robot. and Autom., vol. 1, pp. 3588–3594. Barcelona, Spain (2005)Google Scholar
  71. 71.
    Webster, R.J. III, Romano, J.M., Cowan, N.J.: Mechanics of precurved-tube continuum robots. IEEE Trans. Robot. 25, 67–78 (2009)CrossRefGoogle Scholar
  72. 72.
    Wedlick, T., Okamura, A.: Characterization of pre-curved needles for steering in tissue. In: Proc. IEEE Int. Conf. Eng. Med. Biol. Soc., pp. 1200–1203 (2009)Google Scholar
  73. 73.
    Xu, J., Duindam, V., Alterovitz, R., Goldberg, K.: Motion planning for steerable needles in 3D environments with obstacles using rapidly-exploring random trees and backchaining. In: Proc. IEEE Int. Conf. Automation Sci. and Eng., pp. 41–46 (2008)Google Scholar
  74. 74.
    Yan, K.G., Ng, W.S., Ling, K.V., Yu, Y., Podder, T.: High frequency translational oscillation and rotational drilling of the needle in reducing target movement. In: IEEE Int. Symp. Comp. Intell. in Robot. and Autom., pp. 163–168 (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Noah J. Cowan
    • 1
  • Ken Goldberg
  • Gregory S. Chirikjian
  • Gabor Fichtinger
  • Ron Alterovitz
  • Kyle B. Reed
  • Vinutha Kallem
  • Wooram Park
  • Sarthak Misra
  • Allison M. Okamura
  1. 1.Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations