Hostname: page-component-76fb5796d-5g6vh Total loading time: 0 Render date: 2024-04-25T14:38:27.668Z Has data issue: false hasContentIssue false
  • English
  • Français

A hybrid robot system for CT-guided surgery

Published online by Cambridge University Press:  07 December 2009

Da Liu ,
Tianmiao Wang ,
Can Tang  and
Fan Zhang
Da Liu*
Affiliation:
Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
Tianmiao Wang
Affiliation:
Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
Can Tang
Affiliation:
Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
Fan Zhang
Affiliation:
Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
*
*Corresponding author. E-mail: drliuda@yahoo.com.cn
Get access Rights & Permissions [Opens in a new window]

Summary

The common serial robot or parallel robot is difficult to implement for CT-guided surgery in a limited workspace. A novel hybrid robot with 9 degrees of freedom is presented in this paper, whose detailed structure is analysed based on screw theory and displacement manifold (DM). The dexterity of the hybrid robot is provided in terms of Riemann manifold (RM). Besides, DICOM (digital imaging communications in medicine) image processing, spatial registration and 3D dynamic reconstruction in the operation planning subsystem are analysed, in which some innovative methods are introduced. Meanwhile, the architecture of the CT-guided hybrid robot system and its subsystems are proposed. Simulative clinical experiment showed that the locating precision of the hybrid robot reaches 1.08 mm, which can meet the requirement of CT-guided surgery.

Keywords

Surgical robotCT-guided surgeryHybrid robotScrew theoryRiemann manifoldOperation planningClinical experiment
Type
Article
Information
Robotica , Volume 28 , Special Issue 2: Surgical Robotics: System Development, Application Study and Performance Analysis , March 2010 , pp. 253 - 258
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Zhang, L.-Y. and Yong-Shun, Z., “The development existing state and foreground of medical micro-robot,” Machinery [J]. 44 (506), 33–37 (2006).Google Scholar
2. Alexandru, P. and MD, S. S., “Robotic kidney and spine percutaneous procedures using a new laser-based CT registration method,” Fourth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), IEEE Press, Utrecht (2001) pp. 14–17.Google Scholar
3. Hongwu, W., Yunyou, D., Yanqun, Z. H., Huasong, F., Zhoushan, N., Xia, Y., Haiying, L., Zhihai, H. and Shuping, T., “Percutaneous lung cancer cryotherapy guided by computer tomography,” Journal of Naval Gen. Hosp. 17, 8–15 (2004).Google Scholar
4. Kwoh, Y. S., Hou, J., Jonckheere, E. A. and Hayati, S., “A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery,” Trans. Biomed. Eng. 35 (2), 153–160 (1988).CrossRefGoogle ScholarPubMed
5. Jakopec, M., Harris, S. J., Rodriguez y Baena, F., Gomes, P. and Davies, B. L., “Acrobot: A ‘hands-on’ robot for total knee replacement surgery,” IEEE, AMC2002, Maribor, Slovenia (2002) pp. 116–120.Google Scholar
6. Stoianovici, D., Cleary, K., Patriciu, A., Mazilu, D., Stanimir, A., Craciunoiu, N., Watson, V. and Kavoussi, L., “AcuBot: A robot for radiological interventions,” IEEE Trans. Robotics Automat. 19 (5), 927–930 (2003).CrossRefGoogle Scholar
7. Chung, G. B., Lee, S. G., Oh, S. M., Yi, B.-J., Kim, W. K., Kim, Y. S., Park, J. I. and Oh, S. H., “Development of SPINEBOT for spine surgery,” Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (2004) pp. 3942–3947.Google Scholar
8. Wei, L. and Yu-ru, Z., “Dexterity analysis and design of robot for neurosurgery,” Mach. Des. Res. 22, 39–45 (2006).Google Scholar
9. Tingli, Y., Topology Structure Design of Robot Mechanisms (Mechanical Industry Press, Beijing, 2004).Google Scholar
10. Zhen, H., Lingfu, K. and Yuefa, F., Mechanism Theory and Control of Parallel Robot (Mechanical Industry Press, Beijing, 1997).Google Scholar
11. Murray, R. M., Li, Z. and Sastry, S. S., A Mathematical Introduction to Robotic Manipulation (CRC Press, Boca Raton, FL, 2000).Google Scholar
12. Sorli, M., Ferraresi, C., Kolarski, M., Borovac, B. and Vukobratovic, M., “Mechanics of Turin parallel robot,” Mech. Mach. Theory 32, 51–77 (1997).CrossRefGoogle Scholar
13. Qinchuan, L., “Type synthesis theory of lower-mobility parallel mechanisms and synthesis of new architectures,” Qin Huangdao (2003) pp. 103–114.Google Scholar
14. Qishao, L., Modern Math Foundation (Beijing: Beihang University Press, 2001) pp. 168–171.Google Scholar
15. Sederberg, T. W. and Eugene, G., “A physically based approach to 2D shape blending,” Comput. Graph. 26, 25–34 (1992).CrossRefGoogle Scholar
16. Sederberg, T. W., Peisheng, G., Guojin, W. and Hong, M., “2D shape blending: An intrinsic solution to the vertex path problem,” Comput. Graph. 27, 15–18 (1993).Google Scholar
17. Shapira, M. and Rappoport, A.. “Shape blending using the star-skeleton representation,” IEEE Comput. Graph. Appl. 15, 44–51 (1995).CrossRefGoogle Scholar
18. Iben, H. N., O'Brien, J. F. and Erik, D. D., “Refolding planar polygons,” Proceedings of the 2006 Symposium on Computational Geometry, Sedona, Arizona (June 2006) pp. 71–79.Google Scholar
19. Chen, H., Cao, T., Liu, D. and Tang, C., “An efficient method to realize space registration in image-guided robot-assisted surgery,” ICIRA 2008, Part I, LNAI 5314, Wuhan, China (Oct. 2008) pp. 744–752.Google Scholar