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Soft Tissue Characterisation Using a Force Feedback-Enabled Instrument for Robotic Assisted Minimally Invasive Surgery Systems

  • Mohsen Moradi Dalvand
  • Bijan Shirinzadeh
  • Saeid Nahavandi
  • Fatemeh Karimirad
  • Julian Smith
Conference paper

Abstract

An automated laparoscopic instrument capable of non-invasive measurement of tip/tissue interaction forces for direct application in robotic assisted minimally invasive surgery systems is introduced in this chapter. It has the capability to measure normal grasping forces as well as lateral interaction forces without any sensor mounted on the tip jaws. Further to non-invasive actuation of the tip, the proposed instrument is also able to change the grasping direction during surgical operation. Modular design of the instrument allows conversion between surgical modalities (e.g., grasping, cutting, and dissecting). The main focus of this paper is on evaluation of the grasping force capability of the proposed instrument. The mathematical formulation of fenestrated insert is presented and its non-linear behaviour is studied. In order to measure the stiffness of soft tissues, a device was developed that is also described in this chapter. Tissue characterisation experiments were conducted and results are presented and analysed here. The experimental results verify the capability of the proposed instrument in accurately measuring grasping forces and in characterising artificial tissue samples of varying stiffness.

Keywords

Actuation mechanism Force measurement Laparoscopic instrument Modularity Power transmission mechanism Robotic assisted minimally invasive surgery (RAMIS) Strain gages 

Notes

Acknowledgement

This research is funded by Australian Research Council, ARC Discovery-DP0986814, ARC LIEF-LE0668508, and ARC LIEF-LE0453629.

References

  1. 1.
    A.R. Lanfranco, A.E. Castellanos, J.P. Desai, W.C. Meyers, Robotic surgery: a current perspective. Ann. Surg. 239, 14 (2004)CrossRefGoogle Scholar
  2. 2.
    M. Moradi Dalvand, B. Shirinzadeh, J. Smith, Effects of realistic force feedback in a robotic assisted minimally invasive surgery system. Minimally Invasive Therapy and Allied Technologies (MITAT) (2013) (in press)Google Scholar
  3. 3.
    S.M. Sukthankar, N.P. Reddy, Towards force feedback in laparoscopic surgical tools, in Proceedings of the 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. IEEE (1994) Google Scholar
  4. 4.
    C.E. Reiley et al., Effects of visual force feedback on robot-assisted surgical task performance. J. Thorac. Cardiovasc. Surg. 135, 196–202 (2008)CrossRefGoogle Scholar
  5. 5.
    M. Fakhry, F. Bello, G.B. Hanna, A real-time compliance mapping system using standard endoscopic surgical forceps. IEEE Trans. Biomed. Eng. 56, 1245–1253 (2009)CrossRefGoogle Scholar
  6. 6.
    P. Lamata et al., Understanding perceptual boundaries in laparoscopic surgery. IEEE Trans. Biomed. Eng. 55, 866–873 (2008)CrossRefGoogle Scholar
  7. 7.
    P. Dubois, Q. Thommen, A.C. Jambon, In vivo measurement of surgical gestures. IEEE Trans. Biomed. Eng. 49, 49–54 (2002)CrossRefGoogle Scholar
  8. 8.
    J. Rosen, B. Hannaford, C.G. Richards, M.N. Sinanan, Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans. Biomed. Eng. 48, 579–591 (2001)CrossRefGoogle Scholar
  9. 9.
    S.K. Prasad et al., in Medical Image Computing and Computer-Assisted Intervention, pp. 279–286Google Scholar
  10. 10.
    J. Dargahi, M. Parameswaran, S. Payandeh, A micromachined piezoelectric tactile sensor for an endoscopic grasper: theory, fabrication and experiments. J. Microelectromech. Syst. 9, 329–335 (2000)CrossRefGoogle Scholar
  11. 11.
    U. Seibold, B. Kuebler, G. Hirzinger, Prototype of instrument for minimally invasive surgery with 6-axis force sensing capability, in ICRA (2005)Google Scholar
  12. 12.
    B. Kuebler, U. Seibold, G. Hirzinger, Development of actuated and sensor integrated forceps for minimally invasive robotic surgery. Int. J. Med. Robot. Comput. Assist. Surg. 1, 96–107 (2005)CrossRefGoogle Scholar
  13. 13.
    P. Valdastri et al., Integration of a miniaturised triaxial force sensor in a minimally invasive surgical tool. IEEE Trans. Biomed. Eng. 53, 2397–2400 (2006)CrossRefGoogle Scholar
  14. 14.
    M. Mahvash et al., Modeling the forces of cutting with scissors. IEEE Trans. Biomed. Eng. 55, 848–856 (2008)CrossRefGoogle Scholar
  15. 15.
    M.C. Yip, S.G. Yuen, R.D. Howe, A robust uniaxial force sensor for minimally invasive surgery. IEEE Trans. Biomed. Eng. 57, 1008–1011 (2010). doi: 10.1109/tbme.2009.2039570 CrossRefGoogle Scholar
  16. 16.
    J. Rosen, B. Hannaford, M.P. MacFarlane, M.N. Sinanan, Force controlled and teleoperated endoscopic grasper for minimally invasive surgery-experimental performance evaluation. IEEE Trans. Biomed. Eng. 46, 1212–1221 (1999)CrossRefGoogle Scholar
  17. 17.
    G. Tholey, J.P. Desai, A modular, automated laparoscopic grasper with three-dimensional force measurement capability, in IEEE International Conference on Robotics and Automation. IEEE (2007)Google Scholar
  18. 18.
    C.R. Wagner, N. Stylopoulos, R.D. Howe, The Role of Force Feedback in Surgery: Analysis of Blunt Dissection, in Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2002)Google Scholar
  19. 19.
    M. MacFarlane, J. Rosen, B. Hannaford, C. Pellegrini, M. Sinanan, Force-feedback grasper helps restore sense of touch in minimally invasive surgery. J. Gastrointest. Surg. 3, 278–285 (1999)CrossRefGoogle Scholar
  20. 20.
    A.J. Madhani, G. Niemeyer, J.K. Salisbury, The black falcon: a teleoperated surgical instrument for minimally invasive surgery, in Proceedings on 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2. IEEE (1998)Google Scholar
  21. 21.
    J. Rosen, J.D. Brown, L. Chang, M.N. Sinanan, B. Hannaford, Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans. Biomed. Eng. 53, 399–413 (2006)CrossRefGoogle Scholar
  22. 22.
    S. Shimachi, Y. Hakozaki, T. Tada, Y. Fujiwara, Measurement of force acting on surgical instrument for force-feedback to master robot console, in International Congress Series, vol. 1256. Elsevier (2003)Google Scholar
  23. 23.
    M. Moradi Dalvand, B. Shirinzadeh, S. Nahavandi, F. Karimirad, J. Smith, in Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science, WCECS 2013, pp. 419–424 (2013)Google Scholar
  24. 24.
    M. Moradi Dalvand, B. Shirinzadeh, Forward kinematics analysis of offset 6-RRCRR parallel manipulators, in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, 3011–3018 (2011)Google Scholar
  25. 25.
    M. Moradi Dalvand, B. Shirinzadeh, Motion control analysis of a parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS). Robot. Comput.-Integr. Manuf. 29, 318–327 (2013). doi: 10.1016/j.rcim.2012.09.003 CrossRefGoogle Scholar
  26. 26.
    M. Moradi Dalvand, B. Shirinzadeh, Remote centre-of-motion control algorithms of 6-RRCRR parallel robot assisted surgery system (PRAMiSS), in IEEE International Conference on Robotics and Automation (ICRA), IEEE (2012)Google Scholar
  27. 27.
    T. Hu, A. Castellanos, G. Tholey, J. Desai, Real-time haptic feedback in laparoscopic tools for use in gastro-intestinal surgery, in Medical Image Computing and Computer-Assisted Intervention—MICCAI, pp. 66–74 (2002)Google Scholar
  28. 28.
    M. Tavakoli, R. PateI, M. Moallem, in Proceedings of IEEE International Conference on Robotics and Automation, ICRA’04, pp. 371–376 (2004)Google Scholar
  29. 29.
    M.J. Uddin, Y. Nasu, K. Takeda, S. Nahavandi, G. Capi, An autonomous trimming system of large glass fiber reinforced plastics parts using an omni-directional mobile robot and its control, in Proceedings: Eight International Conference on Manufacturing and Management: Operations Management and Advanced Technology: Integration for Success. PCMM (2004)Google Scholar
  30. 30.
    S.B. Kesner, R.D. Howe, Position control of motion compensation cardiac catheters. IEEE Trans. Robot. 27, 1045–1055 (2011)CrossRefGoogle Scholar
  31. 31.
    S.G. Yuen, N.V. Vasilyev, P.J. del Nido, R.D. Howe, Robotic tissue tracking for beating heart mitral valve surgery. Med. Image Anal. 17, 1236–1242 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Mohsen Moradi Dalvand
    • 1
  • Bijan Shirinzadeh
    • 2
  • Saeid Nahavandi
    • 1
  • Fatemeh Karimirad
    • 1
  • Julian Smith
    • 3
  1. 1.Centre for Intelligent Systems Research (CISR)Deakin UniversityMelbourneAustralia
  2. 2.Robotics and Mechatronics Research Laboratory (RMRL), Department of Mechanical and Aerospace EngineeringMonash UniversityMelbourneAustralia
  3. 3.Department of SurgeryMonash Medical Centre, Monash UniversityMelbourneAustralia

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