Advertisement

Multilevel Fuzzy Control Based on Force Information in Robot-Assisted Decompressive Laminectomy

  • Xiaozhi Qi
  • Yu Sun
  • Xiaohang Ma
  • Ying Hu
  • Jianwei Zhang
  • Wei Tian
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)

Abstract

The lumbar spinal stenosis (LSS) is a kind of orthopedic disease which causes a series of neurological symptom. Vertebral lamina grinding operation is a key procedure in decompressive laminectomy for LSS treatment. With the help of image-guided navigation system, the robot-assisted technology is applied to reduce the burdens on surgeon and improve the accuracy of the operation. This paper proposes a multilevel fuzzy control based on force information in the robot-assisted decompressive laminectomy to improve the quality and the robotic dynamic performance in surgical operation. The controlled grinding path is planned in the medical images after 3D reconstruction, and the mapping between robot and images is realized by navigation registration. Multilevel fuzzy controller is used to adjust the feed rate to keep the grinding force stable. As the vertebral lamina contains different components according to the anatomy, it has different mechanical properties as the main reason causing the fluctuation of force. A feature extraction method for texture recognition of bone is introduced to improve the accuracy of component classification. When the inner cortical bone is reached, the feeding operation needs to stop to avoid penetration into spinal cord and damage to the spinal nerves. Experiments are conducted to evaluate the dynamic stabilities of the control system and state recognition.

Keywords

Decompressive laminectomy Surgical robot Multilevel fuzzy control State recognition 

Notes

Acknowledgments

This work is financially supported by the National Natural Science Foundation of China (Grant Nos. U1613224, U1713221 and 61573336) and the National Key R&D Program of China (Grant No. 2017YFC0110600), in part by Shenzhen Fundamental Research Funds (Grant Nos. JCYJ20150529143500954, JCYJ20160608153218487, JCYJ20170307170252420 and JCYJ20160229202315086) and Shenzhen Key Laboratory Project (Grant No. ZDSYS201707271637577).

References

  1. 1.
    Bertelsen A, Melo J, Sánchez E, Borro D (2013) A review of surgical robots for spinal interventions. Int J Med Robot 9:407–422.  https://doi.org/10.1002/rcs.1469 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Chad DA (2007) Lumbar spinal stenosis. Neurol Clin 25:407–418CrossRefPubMedCentralGoogle Scholar
  3. 3.
    Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27CrossRefGoogle Scholar
  4. 4.
    Chen X, Varley MR, Shark LK, Shentall GS, Kirby MC (2008) A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data. Phys Med Biol 53: 967–983CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Chung GB, Lee SG, Oh SM, Yi BJ (2004) Development of SPINEBOT for spine surgery. In: IEEE/RSJ international conference on intelligent robots and systems, vol 4, pp 3942–3947Google Scholar
  6. 6.
    Chung GB, Lee SG, Kim S, Yi BJ, Kim WK, Oh SM, Kim YS, Park JI, Oh SH (2005) A robot-assisted surgery system for spinal fusion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp 3015–3021Google Scholar
  7. 7.
    Chung GB, Kim S, Lee SG, Yi BJ, Kim W, Oh SM, Kim YS, So BR, Park JI, Oh SH (2006) An image-guided robotic surgery system for spinal fusion. Int J Control Autom Syst 4:30–41Google Scholar
  8. 8.
    Deng Z, Jin H, Hu Y, He Y, Zhang P, Tian W, Zhang J (2016) Fuzzy force control and state detection in vertebral lamina grinding. Mechatronics 35:1–10CrossRefGoogle Scholar
  9. 9.
    Fan L, Gao P, Zhao B, Sun Y, Xin X, Hu Y, Liu S, Zhang J (2016) Safety control strategy for vertebral Lamina grinding task. Caai Trans Intell TechnolGoogle Scholar
  10. 10.
    Foley K, Simon D, Rampersaud Y (2001) Virtual fluoroscopy: computer-assisted fluoroscopic navigation. Spine 26:347CrossRefPubMedCentralGoogle Scholar
  11. 11.
    Holly LT (2006) Image-guided spinal surgery. Int J Med Robot 2:7–15CrossRefPubMedCentralGoogle Scholar
  12. 12.
    Inoue T, Sugita N, Mitsuishi M, Saito T (2010) Optimal control of cutting feed rate in the robotic grinding for total knee arthroplasty. In: IEEE Ras and Embs International Conference on Biomedical Robotics and Biomechatronics. pp 215–220Google Scholar
  13. 13.
    Kim EH, Kim HT (2009) En bloc partial laminectomy and posterior lumbar interbody fusion in Foraminal spinal stenosis. Asian Spine J 3:66–72.  https://doi.org/10.4184/asj.2009.3.2.66 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kwoh YS (1985) A new computerized tomographic aided robotic stereotactic system. Robot Age 7:17–21Google Scholar
  15. 15.
    Kwoh YS, Hou J, Jonckheere EA, Hayati S (1988) A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng 35:153CrossRefPubMedCentralGoogle Scholar
  16. 16.
    Lee CC (1990) Fuzzy logic in control systems: fuzzy logic. Parts I and II, IEEE Trans. IEEE Trans Syst Man Cybern 20:404–418CrossRefGoogle Scholar
  17. 17.
    Lei W, Xin G, Qiang F (2013) A novel mutual information-based similarity measure for 2D/3D registration in image guided intervention. In: International Conference on Orange Technologies. pp 135–138Google Scholar
  18. 18.
    Luan S, Wang T, Li W, Liu Z, Jiang L, Hu L (2012) 3D navigation and monitoring for spinal grinding operation based on registration between multiplanar fluoroscopy and CT images. Comput Methods Prog Biomed 108:151–157CrossRefGoogle Scholar
  19. 19.
    Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16:642–661CrossRefPubMedCentralGoogle Scholar
  20. 20.
    Mclaughlin RA, Hipwell J, Hawkes DJ, Noble JA, Byrne JV, Cox TCS (2002) A comparison of 2D-3D intensity-based registration and feature-based registration for Neurointerventions. Med Image Comput Comput-Assist Interv 2489:517–524Google Scholar
  21. 21.
    Nolte LP, Visarius H, Arm E, Langlotz F, Schwarzenbach O, Zamorano L (1995a) Computer-aided fixation of spinal implants. J Image Guid Surg 1:88–93CrossRefPubMedCentralGoogle Scholar
  22. 22.
    Nolte LP, Zamorano L, Visarius H, Berlemann U, Langlotz F, Arm E, Schwarzenbach O (1995b) Clinical evaluation of a system for precision enhancement in spine surgery. Clin Biomech 10:293CrossRefGoogle Scholar
  23. 23.
    Nolte LP, Slomczykowski MA, Berlemann U, Strauss MJ, Hofstetter R, Schlenzka D, Laine T, Lund T (2000) A new approach to computer-aided spine surgery: fluoroscopy-based surgical navigation. Eur Spine J 9:S078–S088CrossRefGoogle Scholar
  24. 24.
    P MFCAVDMGS (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198CrossRefGoogle Scholar
  25. 25.
    Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004CrossRefPubMedCentralGoogle Scholar
  26. 26.
    Russakoff DB, Rohlfing T, Mori K, Rueckert D, Ho A, Adler JR, Maurer CR (2005) Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration. IEEE Trans Med Imaging 24:1441CrossRefPubMedCentralGoogle Scholar
  27. 27.
    Santos-Munné JJ, Peshkin MA, Mirkovic S, Stulberg SD, Iii TCK (1995) A stereotactic/robotic system for pedicle screw placementGoogle Scholar
  28. 28.
    Sautot P, Cinquin P, Lavallee S, Troccaz J (1992) Computer assisted spine surgery: a first step toward clinical, application in orthopaedics. In: Engineering in Medicine and Biology Society, 1992 International Conference of the IEEE. pp 1071–1072Google Scholar
  29. 29.
    Shoham M, Burman M, Zehavi E, Joskowicz L (2003) Bone-mounted miniature robot for surgical procedures: concept and clinical applications. Robot Autom IEEE Trans On 19:893–901CrossRefGoogle Scholar
  30. 30.
    Siddon RL (1985) Fast calculation of the exact radiological path for a three-dimensional CT array. Med Phys 12:252CrossRefPubMedCentralGoogle Scholar
  31. 31.
    Singh K, Vaccaro AR (2012) Pocket atlas of spine surgery. Stuttgart Georg Thieme VerlagGoogle Scholar
  32. 32.
    Stephane Genevay SJA (2010) Lumbar Spinal Stenosis. Best Pract Res Clin Rheumatol 24:253–265CrossRefPubMedCentralGoogle Scholar
  33. 33.
    Sugita N, Genma F, Nakajima Y, Mitsuishi M (2007) Adaptive controlled grinding robot for orthopedic surgery. In: IEEE International Conference on Robotics and Automation. pp 605–610Google Scholar
  34. 34.
    Sugita N, Nakano T, Nakajima Y, Fujiwara K, Abe N, Ozaki T, Suzuki M, Mitsuishi M (2009) Dynamic controlled grinding process for bone machining. J Mater Process Technol 209:5777–5784CrossRefGoogle Scholar
  35. 35.
    Sugita N, Nakano T, Kato T, Nakajima Y, Mitsuishi M (2010) Instrument path generator for bone machining in minimally invasive orthopedic surgery. IEEEASME Trans Mechatron 15:471–479CrossRefGoogle Scholar
  36. 36.
    Sundermann E, Jacobs F, Christiaens M, Sutter BD, Lemahieu I (1998) A fast algorithm to calculate the exact radiological path through a pixel or voxel space. J Comput Inf Technol 6:89–94Google Scholar
  37. 37.
    Szpalski M, Gunzburg R (2003) Lumbar spinal stenosis in the elderly: an overview. Eur Spine J 12:S170–S175CrossRefPubMedCentralGoogle Scholar
  38. 38.
    Taylor RH, Mittelstadt BD, Paul HA, Hanson W, Kazanzides P, Zuhars JF, Williamson B, Musits BL, Glassman E, Bargar WL (1994) An image-directed robotic system for precise orthopaedic surgery. IEEE Trans Robot Autom 10:261–275CrossRefGoogle Scholar
  39. 39.
    Tjardes T, Shafizadeh S, Rixen D, Paffrath T, Bouillon B, Steinhausen ES, Baethis H (2010) Image-guided spine surgery: state of the art and future directions. Eur Spine J 19:25–45CrossRefPubMedCentralGoogle Scholar
  40. 40.
    Wang L, Gao X, Zhang R, Xia W (2014) A comparison of two novel similarity measures based on mutual information in 2D/3D image registration. In: IEEE International Conference on Medical Imaging Physics and Engineering. pp 215–218Google Scholar
  41. 41.
    Xu C, Shin YC (2005) Design of a multilevel fuzzy controller for nonlinear systems and stability analysis. IEEE Trans Fuzzy Syst 13:761–778CrossRefGoogle Scholar
  42. 42.
    Xu C, Shin YC (2008) An adaptive fuzzy controller for constant cutting force in end-grinding processes. J Manuf Sci Eng 130:683–695Google Scholar
  43. 43.
    Yen PL, Hung SS (2010) An intelligent bone cutting instrument in robot-assisted knee replacement. In: Sice conference 2010, proceedings of. pp 1894–1899Google Scholar
  44. 44.
    Yen PL, Tsai CH (2007) Cooperative force control of a knee surgical robot for lateral grinding of bone. In: IEEE Workshop on Advanced Robotics and ITS Social Impacts. pp 1–6Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xiaozhi Qi
    • 1
  • Yu Sun
    • 1
  • Xiaohang Ma
    • 1
  • Ying Hu
    • 1
  • Jianwei Zhang
    • 2
  • Wei Tian
    • 3
  1. 1.Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and SystemShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.University of HamburgHamburgGermany
  3. 3.Beijing Jishuitan HospitalBeijingChina

Personalised recommendations