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Design and Experiment of Actuator for Semi-automatic Human-Machine Collaboration Percutaneous Surgery

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Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13014))

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Abstract

This paper designs a manual percutaneous puncture actuator, and proposes a semi-automatic human-machine collaboration workflow that fully integrates the high precision of the robot and the flexibility of the doctor. This paper verifies the second-order polynomial relationship between the friction force of the needle in the tissue and the deflection through experiments. In the framework of the workflow proposed in this paper, combined with the damage-minimizing puncture method proposed in this paper, manual high-precision puncture is realized, and the accuracy of manual puncture is improved by 86% when the penetration depth is 120 mm.

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References

  1. Hiraki, T., et al.: Robotic needle insertion during computed tomography fluoroscopy–guided biopsy: prospective first-in-human feasibility trial. Eur. Radiol. 30(2), 927–933 (2019). https://doi.org/10.1007/s00330-019-06409-z

    Article  Google Scholar 

  2. Kwoh, Y.S., Hou, J., Jonckheere, E.A., Hayati, S.: A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans. Biomed. Eng. 35, 153–160 (1988). https://doi.org/10.1109/10.1354

    Article  Google Scholar 

  3. Haidegger, T.: Autonomy for surgical robots: concepts and paradigms. IEEE Trans. Med. Robot. Bionics 1, 65–76 (2019). https://doi.org/10.1109/TMRB.2019.2913282

    Article  Google Scholar 

  4. Abedin-Nasab, M.H.: Handbook of Robotic and Image-Guided Surgery. Elsevier, Amsterdam (2019)

    Google Scholar 

  5. Podder, T.K., et al.: AAPM and GEC‐ESTRO guidelines for image‐guided robotic brachytherapy: report of task group 192. Med. Phys. 41 (2014)

    Google Scholar 

  6. Dou, H., Jiang, S., Yang, Z., Sun, L., Ma, X., Huo, B.: Design and validation of a CT-guided robotic system for lung cancer brachytherapy. Med. Phys. 44, 4828–4837 (2017). https://doi.org/10.1002/mp.12435

    Article  Google Scholar 

  7. Remebot. https://remebot.com.cn/index.php/site/index

  8. Yi, L.: Research on key technology of radioactive seed implantation robot for prostate (2017)

    Google Scholar 

  9. Abolhassani, N., Patel, R.V., Ayazi, F.: Minimization of needle deflection in robot-assisted percutaneous therapy. Int. J. Med. Robot. Comput. Assist. Surg. 3, 140–148 (2007)

    Article  Google Scholar 

  10. Li, P., Yang, Z., Jiang, S.: Needle-tissue interactive mechanism and steering control in image-guided robot-assisted minimally invasive surgery: a review. Med. Biol. Eng. Comput. 56(6), 931–949 (2018). https://doi.org/10.1007/s11517-018-1825-0

    Article  Google Scholar 

  11. Roberti, A., Piccinelli, N., Meli, D., Muradore, R., Fiorini, P.: Improving rigid 3-D calibration for robotic surgery. IEEE Trans. Med. Robot. Bionics 2, 569–573 (2020). https://doi.org/10.1109/TMRB.2020.3033670

    Article  Google Scholar 

  12. Okamura, A.M., Simone, C., O’Leary, M.D.: Force modeling for needle insertion into soft tissue. IEEE Trans. Biomed. Eng. 51, 1707–1716 (2004). https://doi.org/10.1109/TBME.2004.831542

    Article  Google Scholar 

  13. Yang, C.J., Xie, Y., Liu, S., Sun, D.: Force modeling, identification, and feedback control of robot-assisted needle insertion: a survey of the literature. Sensors 18, 38 (2018). https://doi.org/10.3390/s18020561

    Article  Google Scholar 

  14. Lehmann, T., Rossa, C., Usmani, N., Sloboda, R., Tavakoli, M.: A real-time estimator for needle deflection during insertion into soft tissue based on adaptive modeling of needle-tissue interactions. IEEE/ASME Trans. Mechatron., 1–1 (2016)

    Google Scholar 

  15. Webster, R.J., Cowan, N.J., Chirikjian, G., Okamura, A.M.: Nonholonomic modeling of needle steering. Int. J. Robot. Res. 25, 509–525 (2006)

    Google Scholar 

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Acknowledgements

This research was funded by National Key Research and Development Program of China (Grant No. 2019YFB1311303), Natural Science Foundation of China (Grant No. U1713202) and Major scientific and technological innovation projects in Shandong Province (Grant No. 2019JZZY010430).

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Correspondence to Leifeng Zhang .

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Zhang, L., Li, C., Fan, Y., Liu, G., Zhang, X., Zhao, J. (2021). Design and Experiment of Actuator for Semi-automatic Human-Machine Collaboration Percutaneous Surgery. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-89098-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89097-1

  • Online ISBN: 978-3-030-89098-8

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