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Design, Modeling, and Evaluation of a Compact and Lightweight Needle End-effector with Simple Force-feedback Implementation for Robotic CT-guided Needle Interventions

  • Kiyoung Kim
  • Hyunsoo Woo
  • Jang Ho Cho
  • Jungwook SuhEmail author
Article
  • 71 Downloads

Abstract

We present a compact and lightweight two degrees-of-freedom (DOF) needle end-effector to be applied to a teleoperated needle interventional robotic system. The suggested needle end-effector is computerized tomography (CT)-compatible and easy to sterilize. Because the proposed needle end-effector can be attached to a robotic manipulator, the needle can translate into and rotate in the human body. In addition, a 1-axis load cell is attached to the needle end-effector to measure the longitudinal force applied to the needle. With this force measurement, force-feedback ability has been implemented in the entire master-slave robotic system. Basic performance tests of repeatability and maximum insertion force were performed. Several force-feedback experiments to determine free space response and contact response, as well as a discrimination test, were conducted. Through this verification process, the suggested needle end-effector was found to have potential for application in real environments that require robotic CT-guided needle interventions.

Keywords

Force-feedback medical robot needle end-effector robotic CT-guided intervention 

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  1. [1]
    F. Heinrich, F. Joeres, K. Lawonn, and C. Hansen, “Comparison of projective augmented reality concepts to support medical needle insertion,” IEEE Trans. Vis. Comput. Graph., vol. 25, no. 6, pp. 2157–2167, 2019.CrossRefGoogle Scholar
  2. [2]
    A. K. Han, J. H. Bae, K. C. Gregoriou, C. J. Ploch, R. E. Goldman, G. H. Glover, B. L. Daniel, and M. R. Cutkosky, “MR-compatible haptic display of membrane puncture in robot-assisted needle procedures,” IEEE Trans. Haptics, vol. 11, no. 3, pp. 443–454, 2018.CrossRefGoogle Scholar
  3. [3]
    M. Khadem, C. Rossa, N. Usmani, R. S. Sloboda, and M. Tavakoli, “Robotics-assisted needle steering around anatomical obstacles using notched steerable needles,” IEEE J. Biomed. Health Inform., vol. 22, no. 6, pp. 1917–1928, 2018.CrossRefGoogle Scholar
  4. [4]
    B. Xu, C. Zhou, and S. Y. Ko, “Closed-loop planar fuzzy control system for a curvature-controllable steerable beveltip needle,” Int. J. Control. Autom., vol. 16, no. 5, pp. 2421–2431, 2018.CrossRefGoogle Scholar
  5. [5]
    G. S. Fischer, I. Iordachita, C. Csoma, and G. Fichtinger, “MRI-compatible pneumatic robot for transperineal prostate needle placement,” IEEE/ASME Trans. on Mechatron., vol. 13, no. 3, pp. 295–305, 2008.CrossRefGoogle Scholar
  6. [6]
    A. V. DAmico, R. Cormack, C. M. Tempany, S. Kumar, G. Topulos, H. M. Kooy, and C. N. Coleman, “Real-time magnetic resonance image-guided interstitial brachytherapy in the treatment of select patients with clinically localized prostate cancer,” Int. Journal of Radiat. Oncol. Biol. Phys., vol. 42, pp. 507–515, 1998.CrossRefGoogle Scholar
  7. [7]
    S. Zangos, K. Eichler, K. Engelmann, and T. J. Vogl, “MR-guided transgluteal biopsies with an open low-field system in patients with clinically suspected prostate cancer: Technique and preliminary results,” Eur. Radiol., vol. 15, no. 1, pp. 174–182, 2005.CrossRefGoogle Scholar
  8. [8]
    S. E. Song, N. B. Cho, G. Ficsher, N. Hata, C. M. Tempany, G. Fichtinger, and I. Iordachita, “Development of a pneumatic robot for MRI-guided transperineal prostate biopsy and brachytherapy: new approaches,” In Proc. IEEE Int. Conf. Robotics and Automation, pp. 2580–2585, 2010.Google Scholar
  9. [9]
    R. Kokes, K. Lister, R. Gullapalli, B. Zhang, A. MacMillan, H. Richard, and J. Desai, “Towards a teleoperated needle driver robot with haptic feedback for RFA of breast tumors under continuous MRI,” Med. Image Anal., vol. 13, no. 3, pp. 445–455, 2009.CrossRefGoogle Scholar
  10. [10]
    K. Y. Kim, H. S. Woo, J. H. Cho, and Y. K. Lee, “Development of a two DOF needle driver for CT-guided needle insertion-type interventions,” Proc. of IEEE Int. Sym. Robot and Human Interactive Communication, pp. 470–475, 2017.Google Scholar
  11. [11]
    Y. S. Kwoh, J. Hou, E. Jonckheere, and S. Hayati, “A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery,” IEEE Trans. Biomed. Eng., vol. 35, no. 2, pp. 153–160, 1988.CrossRefGoogle Scholar
  12. [12]
    D. Stoianovici, L. Whitcomb, J. Anderson, R. Taylor, and L. Kavoussi, “A modular surgical robotic system for image guided percutaneous procedures,” Medical Image Computing and Computer-Assisted Intervention, Cambridge, USA, 1998.Google Scholar
  13. [13]
    K. Masamune, G. Fichtinger, A. Patriciu, R. Susil, R. Taylor, L. Kavoussi, J. Anderson, I. Sakuma, T. Dohi, and D. Stoianovici, “System for robotically assisted percutaneous procedures with computer tomography guidance,” Comput. Aided Surg., vol. 6, pp. 370–383, 2001.CrossRefGoogle Scholar
  14. [14]
    B. Maurin, B. Bayle, O. Piccin, J. Gangloff, M. de Mathelin, C. Doignon, P. Zanne, and A. Gangi, “A patient-mounted robotic platform for CT-scan guided procedures,” IEEE Trans. Biomed. Eng., vol. 55, no. 10, pp. 2417–2425, 2008.CrossRefGoogle Scholar
  15. [15]
    Y. Moon, J. B. Seo, and J. Choi, “Development of new end-effector for proof-of-concept of fully robotic multichannel biopsy,” IEEE/ASME Trans. Mechatron., vol. 20, no. 6, pp. 2996–3008, 2015.CrossRefGoogle Scholar
  16. [16]
    H. Cha, J. Chung, W. K. Kim, and B. Yi, “Master-slave robotic system for 3 dimensional needle steering,” Proc. of IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 857–862, 2010.Google Scholar
  17. [17]
    Q. C. Nguyen, Y. Kim, and H. Kwon, “Optimization of layout and path planning of surgical robotic system,” Int. J. Control. Autom., vol. 15, no. 1, pp. 375–384, 2017.CrossRefGoogle Scholar
  18. [18]
    D. D. Lorenzo, Y. Koseki, E. D. Momi, K. Chinzei, and A. M. Okamura, “Coaxial needle insertion assistant with enhanced force feedback,” IEEE Trans. Biomed. Eng., vol. 60, no. 2, pp. 379–389, 2012.CrossRefGoogle Scholar
  19. [19]
    O. Piccin, L. Barbe, B. Bayle, M. de Mathelin, A. Gangi, “A force feedback teleoperated needle insertion device for percutaneous procedures,” Int. J. Robot. Res., vol. 28, no. 9, pp. 1154–1168, 2009.CrossRefGoogle Scholar
  20. [20]
    H. Song, K. Kim, and J. Lee, “Development of optical fiber bragg grating force-reflection sensor system of medical application for safe minimally invasive robotic surgery,” Rev. Sci. Instrum., vol. 82, no. 7, pp. 074301–8, 2011.CrossRefGoogle Scholar
  21. [21]
    A. J. Madhani, G. Niemeyer, and J. K. Salisbury Jr., “The Black Falcon: A teleoperated surgical instrument for minimally invasive surgery,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Victoria, Canada, pp. 936–944, 1998.Google Scholar
  22. [22]
    K. Kim, J. Lee, “Design and evaluation of a slave manipulator with roll-pitch-roll wrist and automatic tool loading mechanism in telerobotic surgery,” Int. J. Med. Robot., vol. 8, no. 4, pp. 421–435, 2012.CrossRefGoogle Scholar
  23. [23]
    H. A. Mansy, J. R. Grahe, and R. H. Sandler, “Elastic properties of synthetic materials for soft tissue modeling,” Phys. Med. Biol., vol. 53, no. 8, pp. 2115–2130, 2008.CrossRefGoogle Scholar
  24. [24]
    R. M. Murray, Z. Li, and S. S. Sastry, A Mathematical Introduction to Robotic Manipulation, CRC Press, Berkeley, 1994.zbMATHGoogle Scholar
  25. [25]
    M. Dede and S. Tosunoglu, “Fault-tolerant teleoperation systems design,” Ind. Robot., vol. 33, no. 5, pp. 365–372, 2006.CrossRefGoogle Scholar
  26. [26]
    Y. L. Park, S. Elayaperumal, B. Daniel, S. C. Ryu, M. Shin, J. Savall, R. J. Black, B. Moslehi, and M. R. Cutkosky, “Real-time estimation of 3-D needle shape and deflection for MRI-guided interventions,” IEEE/ASME Trans. Mechatron., vol. 15, no. 6, pp. 906–914, 2010.Google Scholar
  27. [27]
    L. Barbe, B. Bernard, M. de Michel, and G. Afshin, “In vivo model estimation and haptic characterization of needle insertions,” Int. J. Robot. Res., vol. 26, pp. 1283–1301, 2007.CrossRefGoogle Scholar
  28. [28]
    P. Kim, S. Kim, S. H. Choi, J. S. Oh, and S. B. Choi, “Force modeling for incision surgery into tissue with haptic application,” SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring. International Society for Optics and Photonics; SPIE: Bellingham, WA, USA, 2015.Google Scholar
  29. [29]
    S. R. Lee, C. H. Uhm, M. S. Seong, J. S. Oh, S. B. Choi, “Repulsive force control of minimally invasive surgery robot associated with three degrees of freedom electrorheological fluid-based haptic master,” Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci., vol. 228, pp. 1606–1621, 2013.CrossRefGoogle Scholar
  30. [30]
    C. Yang, Y. Xie, S. Liu, and D. Sun, “Force modeling, identification, and feedback control of robot-assisted needle insertion: a survey of the literature,” Sensors, vol. 18, no. 561, pp. 1–38, 2018.Google Scholar
  31. [31]
    S. Jiang, P. Li, Y. Yu, J. Liu, and Z. Yang, “Experimental study of needle-tissue interaction forces: effect of needle geometries, insertion methods and tissue characteristics,” J. Biomech., vol. 47, pp. 3344–3353, 2014.CrossRefGoogle Scholar
  32. [32]
    J. Suh and K. Kim, “Design of a discrete bending joint using multiple unit PREF joints for isotropic 2-DOF motion,” Int. J. Control. Autom., vol. 15, no. 1, pp. 64–72, 2017.MathSciNetCrossRefGoogle Scholar
  33. [33]
    C. Ju and H. I. Son, “Evaluation of haptic feedback in the performance of a teleoperated unmanned ground vehicle in an obstacle avoidance scenario,” Int. J. Control. Autom., vol. 17, no. 1, pp. 168–180, 2019.MathSciNetCrossRefGoogle Scholar
  34. [34]
    A. J. Madhani, Design of Teleoperated Surgical Instruments for Minimally Invasive Surgery, Ph.D. Thesis, MIT, 1998.Google Scholar
  35. [35]
    K. Ogata, Modern Control Engineering, 4th ed., Prentice Hall, 2003.Google Scholar
  36. [36]
    K. B. Reed, A. M. Okamura, and N. J. Cowan, “Modeling and control of needles with torsional friction,” IEEE Trans. Biomed. Eng., vol. 56, no. 12, pp. 2905–2916, 2009.CrossRefGoogle Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  • Kiyoung Kim
    • 1
  • Hyunsoo Woo
    • 1
  • Jang Ho Cho
    • 1
  • Jungwook Suh
    • 2
    Email author
  1. 1.Department of Medical Assistant RoboticsKorea Institute of Machinery and Materials (KIMM)DaeguKorea
  2. 2.Department of Robot and Smart System EngineeringKyungpook National University (KNU)DaeguKorea

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