Skip to main content

Advertisement

SpringerLink
Log in

Improved precise guidewire delivery of a cardiovascular interventional surgery robot based on admittance control

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The development of cardiovascular interventional surgery robots can realize master–slave interventional operations, which will effectively solve the problem of surgeons being injured by X-ray radiation. The delivery accuracy and safety of interventional instruments such as guidewire are the most important issues in the development of robotic systems. Most of the current control methods are position control or force feedback control, which cannot take into account delivery accuracy and safety.

Methods

A cardiovascular interventional surgery robotic system integrated force sensors is developed. A novel force/position controller, which includes a radial basis function neural networks-based inner loop position controller and a force-based admittance outer loop controller, is proposed. Furthermore, a series of simulations and vascular model experiments are carried out to demonstrate the feasibility and accuracy of the proposed controller.

Results

The designed cardiovascular interventional robot is flexible to enter the target vessel branch. Experimental results indicate that the proposed controller can effectively improve the delivery accuracy of the guidewire and reduce the contact force with the vessel wall.

Conclusions

The proposed controller based on radial basis function neural network and admittance control is effective in improving delivery accuracy and reducing contact force. The algorithm needs to be further validated in vivo experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Weintraub WS, Spertus JA, Kolm P, Maron DJ, Zhang Z, Jurkovitz C, Zhang W, Hartigan PM, Lewis C, Veledar E (2008) Effect of PCI on quality of life in patients with stable coronary disease. N Engl J Med 359(7):677–687. https://doi.org/10.1056/NEJMoa072771

    Article  CAS  PubMed  Google Scholar 

  2. Klein LW, Miller DL, Balter S, Laskey W, Haines D, Norbash A, Mauro MA, Goldstein JA (2009) Occupational health hazards in the interventional laboratory: time for a safer environment. Radiology 250(2):538–544. https://doi.org/10.1148/radiol.25020825

    Article  PubMed  Google Scholar 

  3. Balter S, Miller DL (2014) Patient skin reactions from interventional fluoroscopy procedures. AJR Am J Roentgenol 202(4):335–42. https://doi.org/10.2214/AJR.13.12029

    Article  Google Scholar 

  4. Weisz G, Metzger DC, Caputo RP, Delgado JA, Marshall JJ, Vetrovec GW, Reisman M, Waksman R, Granada JF, Novack V et al (2013) Safety and feasibility of robotic percutaneous coronary intervention: precise (percutaneous robotically-enhanced coronary intervention) study. J Am Coll Cardiol 61(15):1596–1600. https://doi.org/10.1016/j.jacc.2012.12.045

    Article  PubMed  Google Scholar 

  5. Rafii-Tari H, Payne CJ, Yang G-Z (2014) Current and emerging robot-assisted endovascular catheterization technologies: a review. Ann Biomed Eng 42(4):697–715. https://doi.org/10.1007/s10439-013-0946-8

    Article  PubMed  Google Scholar 

  6. Zhao Y, Mei Z, Luo X, Mao J, Zhao Q, Liu G, Wu D (2022) Remote vascular interventional surgery robotics: a literature review. Quant. Imaging Med. Surg. 12(4):2552. https://doi.org/10.21037/qims-21-792

    Article  PubMed  PubMed Central  Google Scholar 

  7. Granada JF, Delgado JA, Uribe MP, Fernandez A, Blanco G, Leon MB, Weisz G (2011) First-in-human evaluation of a novel robotic-assisted coronary angioplasty system. JACC Cardiovasc Interv 4(4):460–465. https://doi.org/10.1016/j.jcin.2010.12.007

    Article  PubMed  Google Scholar 

  8. Al Nooryani A, Aboushokka W (2018) Rotate-on-retract procedural automation for robotic-assisted percutaneous coronary intervention: First clinical experience. Case Rep Cardiol. https://doi.org/10.1155/2018/6086034

  9. Sankaran NK, Chembrammel P, Siddiqui A, Snyder K, Kesavadas T (2018) Design and development of surgeon augmented endovascular robotic system. IEEE Trans Biomed Eng 65(11):2483–2493. https://doi.org/10.1109/TBME.2018.2800639

    Article  PubMed  Google Scholar 

  10. Shen H, Wang C, Xie L, Zhou S, Gu L, Xie H (2018) A novel remote-controlled robotic system for cerebrovascular intervention. Int J Med Rob Comput Assist Surg 14(6):1943. https://doi.org/10.1002/rcs.1943

    Article  Google Scholar 

  11. Zhou J, Mei Z, Miao J, Mao J, Wang L, Wu D, Sun D, Zhao Y (2020) A remote-controlled robotic system with safety protection strategy based on force-sensing and bending feedback for transcatheter arterial chemoembolization. Micromachines 11(9):805. https://doi.org/10.3390/mi11090805

    Article  PubMed  PubMed Central  Google Scholar 

  12. Guo J, Jin X, Guo S, Fu Q (2019) A vascular interventional surgical robotic system based on force-visual feedback. IEEE Sens J 19(23):11081–11089. https://doi.org/10.1109/JSEN.2019.2935002

    Article  ADS  Google Scholar 

  13. Kweon J, Kim K, Lee C, Kwon H, Park J, Song K, Kim YI, Park J, Back I, Roh J-H et al (2021) Deep reinforcement learning for guidewire navigation in coronary artery phantom. IEEE Access 9:166409–166422. https://doi.org/10.1109/ACCESS.2021.3135277

    Article  Google Scholar 

  14. Wang K, Lu Q, Chen B, Shen Y, Li H, Liu M, Xu Z (2019) Endovascular intervention robot with multi-manipulators for surgical procedures: dexterity, adaptability, and practicability. Rob Comput Integr Manuf 56:75–84. https://doi.org/10.1016/j.rcim.2018.09.004

    Article  CAS  Google Scholar 

  15. Jin X, Guo S, Guo J, Shi P, Tamiya T, Hirata H (2021) Development of a tactile sensing robot-assisted system for vascular interventional surgery. IEEE Sens J 21(10):12284–12294. https://doi.org/10.1109/JSEN.2021.3066424

    Article  ADS  CAS  Google Scholar 

  16. Wang H, Chang J, Yu H, Liu H, Hou C, Lu H (2021) Research on a novel vascular interventional surgery robot and control method based on precise delivery. IEEE Access 9:26568–26582. https://doi.org/10.1109/ACCESS.2021.3057425

    Article  Google Scholar 

  17. Zhou W, Guo S, Guo J, Meng F, Chen Z (2021) ADRC-based control method for the vascular intervention master-slave surgical robotic system. Micromachines 12(12):1439. https://doi.org/10.3390/mi12121439

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hu Z, Zhang J, Xie L, Cui G (2020) A generalized predictive control for remote cardiovascular surgical systems. ISA Trans 104:336–344. https://doi.org/10.1016/j.isatra.2020.05.020

    Article  PubMed  Google Scholar 

  19. Hogan N (1984) Impedance control: an approach to manipulation. In: 1984 American control conference. IEEE, pp 304–313. https://doi.org/10.23919/ACC.1984.4788393

  20. Duan J, Gan Y, Chen M, Dai X (2018) Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot Auton Syst 102:54–65. https://doi.org/10.1016/j.robot.2018.01.009

    Article  Google Scholar 

  21. Peng G, Yang C, He W, Chen CP (2019) Force sensorless admittance control with neural learning for robots with actuator saturation. IEEE Trans Ind Electron 67(4):3138–3148. https://doi.org/10.1109/TIE.2019.2912781

    Article  Google Scholar 

  22. Wu Q, Chen B, Wu H (2019) Adaptive admittance control of an upper extremity rehabilitation robot with neural-network-based disturbance observer. IEEE Access 7:123807–123819. https://doi.org/10.1109/ACCESS.2019.2938566

    Article  Google Scholar 

  23. Franco E, Rea M, Gedroyc W, Ristic M (2016) Control of a master-slave pneumatic system for teleoperated needle insertion in MRI. IEEE/ASME Trans Mechatron 21(5):2595–2600. https://doi.org/10.1109/TMECH.2016.2577608

  24. Jin X, Guo S, Guo J, Shi P, Hirata H (2021) Total force analysis and safety enhancing for operating both guidewire and catheter in endovascular surgery. IEEE Sens J 99:1. https://doi.org/10.1109/JSEN.2021.3107188

    Article  Google Scholar 

  25. Wang S, Liu Z, Shu X, Xie L (2022) Mechanism design and force sensing of a novel cardiovascular interventional surgery robot. Int J Med Rob Comput Assist Surg. https://doi.org/10.1002/rcs.2406

  26. Akinyemi TO, Omisore OM, Chen X, Duan W, Du W, Yi G, Wang L (2022) Adapting neural-based models for position error compensation in robotic catheter systems. Appl Sci 12(21):10936. https://doi.org/10.3390/app122110936

    Article  CAS  Google Scholar 

  27. Du K-L, Swamy M (2006) Radial basis function networks. Neural Netw Softcomput Framew. https://doi.org/10.1007/1-84628-303-5_6

  28. Bao X, Guo S, Xiao N, Li Y, Shi L (2018) Compensatory force measurement and multimodal force feedback for remote-controlled vascular interventional robot. Biomed Microdev 20:1–11. https://doi.org/10.1007/s10544-018-0318-0

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant 62133009, Grant 61973211, and Grant M-0221), in part by the Science and Technology Commission of Shanghai Municipality (Grant 21550714200 and Grant 20DZ2220400), in part by the project of Institute of Medical Robotics of Shanghai Jiao Tong University, in part by the Foreign Cooperation Project of Fujian Province Science and Technology Program (No. 2022I0041), in part by the project of Quanzhou High-level Talent Innovation and Entrepreneurship (No. 2021C003R), in part by the Cooperation Project of Xuhui District Artificial Intelligence Medical Institute (No. 2021-008), and in part by the Joint Project of Xinhua Hospital-Institute of Medical Robotics of Shanghai Jiao Tong University (No. 21XJMR03).

Author information

Authors and Affiliations

  1. Institute of Forming Technology and Equipment, Shanghai Jiao Tong University, Shanghai, 200240, China

    Shuang Wang, Zheng Liu, Yongfeng Cao, Ling Zhang & Le Xie

  2. Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Shuang Wang, Zheng Liu, Yongfeng Cao, Ling Zhang & Le Xie

Authors
  1. Shuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongfeng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Le Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Le Xie.

Ethics declarations

Conflict of interest

All authors declare that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (mp4 24116 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Liu, Z., Cao, Y. et al. Improved precise guidewire delivery of a cardiovascular interventional surgery robot based on admittance control. Int J CARS 19, 209–221 (2024). https://doi.org/10.1007/s11548-023-03017-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-023-03017-7

Keywords

Search

Navigation