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Hand–tool–tissue interaction forces in neurosurgery for haptic rendering

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Abstract

Haptics provides sensory stimuli that represent the interaction with a virtual or tele-manipulated object, and it is considered a valuable navigation and manipulation tool during tele-operated surgical procedures. Haptic feedback can be provided to the user via cutaneous information and kinesthetic feedback. Sensory subtraction removes the kinesthetic component of the haptic feedback, having only the cutaneous component provided to the user. Such a technique guarantees a stable haptic feedback loop, while it keeps the transparency of the tele-operation system high, which means that the system faithfully replicates and render back the user’s directives. This work focuses on checking whether the interaction forces during a bench model neurosurgery operation can lie in the solely cutaneous perception of the human finger pads. If this assumption is found true, it would be possible to exploit sensory subtraction techniques for providing surgeons with feedback from neurosurgery. We measured the forces exerted to surgical tools by three neurosurgeons performing typical actions on a brain phantom, using contact force sensors, while the forces exerted by the tools to the phantom tissue were recorded using a load cell placed under the brain phantom box. The measured surgeon–tool contact forces were 0.01–3.49 N for the thumb and 0.01–6.6 N for index and middle finger, whereas the measured tool–tissue interaction forces were from six to 11 times smaller than the contact forces, i.e., 0.01–0.59 N. The measurements for the contact forces fit the range of the cutaneous sensitivity for the human finger pad; thus, we can say that, in a tele-operated robotic neurosurgery scenario, it would possible to render forces at the fingertip level by conveying haptic cues solely through the cutaneous channel of the surgeon’s finger pads. This approach would allow high transparency and high stability of the haptic feedback loop in a tele-operation system.

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References

  1. Salisbury K, Conti F, Barbagli F (2004) Haptic rendering: introductory concepts. IEEE Comput Graphics Appl 24(2):24–32

    Article  Google Scholar 

  2. Salcudean S, Ku S, Bell G (1997) Performance measurement in scaled teleoperation for microsurgery, in CVRMed-MRCAS’97. Springer, pp. 789–798

  3. De Lorenzo D, De Momi E, Conti L, Votta E, Riva M, Fava E, Bello L, Ferrigno G (2013) Intraoperative forces and moments analysis on patient head clamp during awake brain surgery. Med Biol Eng Comput 51(3):331–341

    Article  PubMed  Google Scholar 

  4. De Lorenzo D, De Momi E, Dyagilev I, Manganelli R, Formaglio A, Prattichizzo D, Shoham M, Ferrigno G (2011) Force feedback in a piezoelectric linear actuator for neurosurgery. Int J Med Robot Comput Assist Surg 7(3):268–275

    Google Scholar 

  5. De Lorenzo D, Koseki Y, De Momi E, Chinzei K, Okamura A (2013) Coaxial needle insertion assistant with enhanced force feedback. IEEE Trans Biomed Eng 60(2):379–389

    Article  PubMed  Google Scholar 

  6. Massimino M, Sheridan T (1994) Teleoperator performance with varying force and visual feedback. Hum Factors J Hum Factors Ergon Soc 36(1):145–157

    CAS  Google Scholar 

  7. Moody L, Baber C, Arvanitis TN et al (2002) Objective surgical performance evaluation based on haptic feedback. Stud Health Technol Inf 85(2):304–310

    Google Scholar 

  8. Wagner C, Stylopoulos N, Howe R (2002) The role of force feedback in surgery: analysis of blunt dissection. In: Symposium on haptic interfaces for virtual environment and teleoperator systems, Citeseer, pp 73–79

  9. Lang M, Sutherland G (2010) Informatic surgery: the union of surgeon and machine. World Neurosurg 74(1):118–120

    Article  PubMed  Google Scholar 

  10. Guidali M, Duschau-Wicke A, Broggi S, Klamroth-Marganska V, Nef T, Riener R (2011) A robotic system to train activities of daily living in a virtual environment. Med Biol Eng Comput 49(10):1213–1223

    Article  PubMed  Google Scholar 

  11. Ellis R, Ismaeil O, Lipsett M (1996) Design and evaluation of a high-performance haptic interface. Robotica 14(03):321–327

    Article  Google Scholar 

  12. Okamura AM (2004) Methods for haptic feedback in teleoperated robot-assisted surgery. Ind Robot Int J 31(6):499–508

    Article  Google Scholar 

  13. Lederman S (1991) Skin and touch. Encycl Hum Biol 7:51–63

    Google Scholar 

  14. Sherrick C, Craig J (1982) The psychophysics of touch: a sourcebook. Tactual perception, p 55

  15. Goethals P (2008) Tactile feedback for robot assisted minimally invasive surgery: an overview. In: Internal Report, Department of Mechanical Engineering KU Leuven

  16. Sherrick C, Cholewiak RW (1986) Cutaneous sensitivity. Handb Percept Hum Perform 1:1–12

    Google Scholar 

  17. Hale K, Stanney K (2004) Deriving haptic design guidelines from human physiological, psychophysical, and neurological foundations. IEEE Comput Graphics Appl 24(2):33–39

    Article  Google Scholar 

  18. Jones L (2000) Kinesthetic sensing. In: Human and machine haptics. MIT Press

  19. Hashtrudi-Zaad K, Salcudean S (2002) Transparency in time-delayed systems and the effect of local force feedback for transparent teleoperation. IEEE Trans Robot Autom 18(1):108–114

    Article  Google Scholar 

  20. Franken M, Stramigioli S, Misra S, Secchi C, Macchelli A (2011) Bilateral telemanipulation with time delays: a two-layer approach combining passivity and transparency. IEEE Trans Robot 27(4):741–756

    Article  Google Scholar 

  21. Bach-y Rim P, Webster J, Tompkins W, Crabb T (1987) Sensory substitution for space gloves and for space robots. In: Proceedings IEEE international conference on robotics and automation, vol 2. ICRA, pp 51–57

  22. Massimino M (1995) Improved force perception through sensory substitution. Control Eng Pract 3(2):215–222

    Article  Google Scholar 

  23. Prattichizzo D, Pacchierotti C, Rosati G (2012) Cutaneous force feedback as a sensory subtraction technique in haptics. IEEE Trans Haptics 5(4):289–300

    Article  CAS  PubMed  Google Scholar 

  24. Pacchierotti C, Tirmizi A, Prattichizzo D (2013) Improving transparency in eleoperation by means of cutaneous tactile force feedback. ACM Trans Appl Percept 11:4

    Google Scholar 

  25. Mihelj M, Podobnik J (2012) Haptic displays. In: Haptics for virtual reality and teleoperation, Series Intelligent Systems, Control and Automation: Science and Engineering, vol 64. Springer, Netherlands, pp 57–73

  26. Miller K, Chinzei K, Orssengo G, Bednarz P (2000) Mechanical properties of brain tissue in-vivo: experiment and computer simulation. J Biomech 33(11):1369–1376

    Article  CAS  PubMed  Google Scholar 

  27. Sutherland G, Wolfsberger S, Lama S, Zarei-nia K (2013) The evolution of neuroArm. Neurosurgery 72:A27–A32

    Article  Google Scholar 

  28. Chen Z, Gillies G, Broaddus W, Prabhu S, Fillmore H, Mitchell R, Corwin F, Fatouros P (2004) A realistic brain tissue phantom for intraparenchymal infusion studies. J Neurosurg 101(2):314–322

    Article  PubMed  Google Scholar 

  29. Marcus H, Zareinia K, Gan L, Yang F, Lama S, Yang G-Z, Sutherland G (2014) Forces exerted during microneurosurgery: a cadaver study. Int J Med Robot Comput Assist Surg 10(2):251–256

    Article  Google Scholar 

  30. Maddahi Y, Gan LS, Zareinia K, Lama S, Sepehri N, Sutherland G (2015) Quantifying workspace and forces of surgical dissection during robot-assisted neurosurgery. Int J Med Robot Comput Assist Surg

  31. Howard M III, Abkes B, Ollendieck M, Noh M, Ritter R, Gillies G (1999) Measurement of the force required to move a neurosurgical probe through in vivo human brain tissue. IEEE Trans Biomed Eng 46(7):891–894

    Article  PubMed  Google Scholar 

  32. Sharp A, Ortega A, Restrepo D, Curran-Everett D, Gall K (2009) In vivo penetration mechanics and mechanical properties of mouse brain tissue at micrometer scales. IEEE Trans Biomed Eng 56(1):45–53

    Article  PubMed  PubMed Central  Google Scholar 

  33. Jensen W, Yoshida K, Hofmann U (2006) In-vivo implant mechanics of flexible, silicon-based acreo microelectrode arrays in rat cerebral cortex. IEEE Trans Biomed Eng 53(5):934–940

    Article  PubMed  Google Scholar 

  34. Rosen J, Hannaford B, Richards CG, Sinanan MN (2001) Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans Biomed Eng 48(5):579–591

    Article  CAS  PubMed  Google Scholar 

  35. Parittotokkaporn T, Frasson L, Schneider A, Huq S, Davies BL, Degenaar P, Biesenack J, Rodriguez y Baena F (2009) Soft tissue traversal with zero net force: feasibility study of a biologically inspired design based on reciprocal motion. In: IEEE international conference on robotics and biomimetics, ROBIO. IEEE, pp 80–85

  36. Ritter RC, Quate E, Gillies G, Grady M, Howard IMA, Broaddus W (1998) Measurement of friction on straight catheters in in vitro brain and phantom material. IEEE Trans Biomed Eng 45(4):476–485

    Article  CAS  PubMed  Google Scholar 

  37. De Lorenzo D, Manganelli R, Dyagilev I, Formaglio A, De Momi E, Prattichizzo D, Shoham M, Ferrigno G (2010) Miniaturized rigid probe driver with haptic loop control for neurosurgical interventions. In: Proceedings IEEE-RAS EMBS international conference on biomedical robotics and biomechatronics, BioRob. IEEE, pp 522–527

  38. LabVIEW System Design Software, National Instruments Corporation. http://www.ni.com/labview/

  39. The OROCOS Project. http://www.orocos.org/

  40. Gefen A, Margulies S (2004) Are in vivo and in situ brain tissues mechanically similar? J Biomech 37(9):1339–1352

    Article  PubMed  Google Scholar 

  41. Prattichizzo D, Trinkle JC (2008) Grasping. In: Springer handbook of robotics. Springer, pp 671–700

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Acknowledgments

The authors acknowledge the contribution of Danilo De Lorenzo for his assistance with the acquisition setup. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007–2013 under Grant Agreement No. 270460 of the project “ACTIVE: Active Constraints Technologies for Ill defined or Volatile Environment” and under Grant agreement No. 601165 of the project “WEARHAP—WEARable HAPtics for humans and robots.”

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Authors and Affiliations

  1. Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100, Siena, Italy

    Marco Aggravi & Domenico Prattichizzo

  2. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

    Elena De Momi & Giancarlo Ferrigno

  3. National Neurological Institute “C. Besta”, Via Celoria 11, 20133, Milan, Italy

    Francesco DiMeco

  4. “Claudio Munari” Epilepsy and Parkinson Surgery Centre, Niguarda Ca Granda Hospital, Piazza Ospedale Maggiore 3, 20162, Milan, Italy

    Francesco Cardinale & Giuseppe Casaceli

  5. Unità of Oncological Neurosurgery Humanitas Research Hospital, Università degli Studi di Milano, via Manzoni 56, 20089, Rozzano, Italy

    Marco Riva

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  1. Marco Aggravi
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  2. Elena De Momi
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Correspondence to Marco Aggravi.

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Aggravi, M., De Momi, E., DiMeco, F. et al. Hand–tool–tissue interaction forces in neurosurgery for haptic rendering. Med Biol Eng Comput 54, 1229–1241 (2016). https://doi.org/10.1007/s11517-015-1439-8

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  • DOI: https://doi.org/10.1007/s11517-015-1439-8

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