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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Chad DA (2007) Lumbar spinal stenosis. Neurol Clin 25:407–418
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27
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: 967–983
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 3942–3947
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 3015–3021
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 4:30–41
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 35:1–10
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 Technol
Foley K, Simon D, Rampersaud Y (2001) Virtual fluoroscopy: computer-assisted fluoroscopic navigation. Spine 26:347
Holly LT (2006) Image-guided spinal surgery. Int J Med Robot 2:7–15
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 215–220
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
Kwoh YS (1985) A new computerized tomographic aided robotic stereotactic system. Robot Age 7:17–21
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 35:153
Lee CC (1990) Fuzzy logic in control systems: fuzzy logic. Parts I and II, IEEE Trans. IEEE Trans Syst Man Cybern 20:404–418
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 135–138
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 108:151–157
Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16:642–661
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 2489:517–524
Nolte LP, Visarius H, Arm E, Langlotz F, Schwarzenbach O, Zamorano L (1995a) Computer-aided fixation of spinal implants. J Image Guid Surg 1:88–93
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 10:293
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–S088
P MFCAVDMGS (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198
Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004
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 24:1441
Santos-Munné JJ, Peshkin MA, Mirkovic S, Stulberg SD, Iii TCK (1995) A stereotactic/robotic system for pedicle screw placement
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 1071–1072
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 19:893–901
Siddon RL (1985) Fast calculation of the exact radiological path for a three-dimensional CT array. Med Phys 12:252
Singh K, Vaccaro AR (2012) Pocket atlas of spine surgery. Stuttgart Georg Thieme Verlag
Stephane Genevay SJA (2010) Lumbar Spinal Stenosis. Best Pract Res Clin Rheumatol 24:253–265
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 605–610
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 209:5777–5784
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 15:471–479
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 6:89–94
Szpalski M, Gunzburg R (2003) Lumbar spinal stenosis in the elderly: an overview. Eur Spine J 12:S170–S175
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 10:261–275
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 19:25–45
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 215–218
Xu C, Shin YC (2005) Design of a multilevel fuzzy controller for nonlinear systems and stability analysis. IEEE Trans Fuzzy Syst 13:761–778
Xu C, Shin YC (2008) An adaptive fuzzy controller for constant cutting force in end-grinding processes. J Manuf Sci Eng 130:683–695
Yen PL, Hung SS (2010) An intelligent bone cutting instrument in robot-assisted knee replacement. In: Sice conference 2010, proceedings of. pp 1894–1899
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 1–6
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Qi, X., Sun, Y., Ma, X., Hu, Y., Zhang, J., Tian, W. (2018). Multilevel Fuzzy Control Based on Force Information in Robot-Assisted Decompressive Laminectomy. In: Zheng, G., Tian, W., Zhuang, X. (eds) Intelligent Orthopaedics. Advances in Experimental Medicine and Biology, vol 1093. Springer, Singapore. https://doi.org/10.1007/978-981-13-1396-7_20
Download citation
DOI: https://doi.org/10.1007/978-981-13-1396-7_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1395-0
Online ISBN: 978-981-13-1396-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)