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A novel catheter interaction simulating method for virtual reality interventional training systems

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Abstract

Endovascular robotic systems have been applied in robot-assisted interventional surgery to improve surgical safety and reduce radiation to surgeons. However, this surgery requires surgeons to be highly skilled at operating vascular interventional surgical robot. Virtual reality (VR) interventional training systems for robot-assisted interventional surgical training have many advantages over traditional training methods. For virtual interventional radiology, simulation of the behaviors of surgical tools (here mainly refers to catheter and guidewire) is a challenging work. In this paper, we developed a novel virtual reality interventional training system. This system is an extension of the endovascular robotic system. Because the master side of this system can be used for both the endovascular robotic system and the VR interventional training system, the proposed system improves training and reduces the cost of education. Moreover, we proposed a novel method to solve catheterization modeling during the interventional simulation. Our method discretizes the catheter by the collision points. The catheter between two adjacent collision points is treated as thin torsion-free elastic rods. The deformation of the rod is mainly affected by the force applied at the collision points. Meanwhile, the virtual contact force is determined by the collision points. This simplification makes the model more stable and reduces the computational complexity, and the behavior of the surgical tools can be approximated. Therefore, we realized the catheter interaction simulation and virtual force feedback for the proposed VR interventional training system. The performance of our method is experimentally validated.

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References

  1. Shi P, Guo S, Zhang L, Jin X, Hirata H, Tamiya T, Kawanishi M (2021) Design and evaluation of a haptic robot-assisted catheter operating system with collision protection function. IEEE Sens J 21(18):20807–20816. https://doi.org/10.1109/JSEN.2021.3095187

    Article  Google Scholar 

  2. Guo S, Song Y, Yin X, Zhang L, Tamiya T, Hirata H, Ishihara H (2019) A novel robot-assisted endovascular catheterization system with haptic force feedback. IEEE Trans Rob 35(3):685–696. https://doi.org/10.1109/TRO.2019.2896763

    Article  Google Scholar 

  3. Yin X, Guo S, Xiao N, Tamiya T, Hirata H, Ishihara H (2016) Safety operation consciousness realization of a MR fluids-based novel haptic interface for teleoperated catheter minimally invasive neurosurgery. IEEE/ASME Trans Mechatron 21(2):1043–1054. https://doi.org/10.1109/TMECH.2015.2489219

    Article  Google Scholar 

  4. 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 Microdevice 20(3):74. https://doi.org/10.1007/s10544-018-0318-0

    Article  CAS  Google Scholar 

  5. Song Y, Guo S, Yin X, Zhang L, Hirata H, Ishihara H, Tamiya T (2018) Performance evaluation of a robot-assisted catheter operating system with haptic feedback. Biomed Microdevice 20(2):50. https://doi.org/10.1007/s10544-018-0294-4

    Article  CAS  Google Scholar 

  6. Zhao Y, Guo S, Wang Y, Cui J, Ma Y, Zeng Y, Liu X, Jiang Y, Li Y, Shi L, Xiao N (2019) A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot. Med Biol Eng Compu 57(9):1875–1887. https://doi.org/10.1007/s11517-019-02002-0

    Article  Google Scholar 

  7. Yang C, Guo S, Bao X, Xiao N, Shi L, Li Y, Jiang Y (2019) A vascular interventional surgical robot based on surgeon’s operating skills. Med Biol Eng Compu 57(9):1999–2010. https://doi.org/10.1007/s11517-019-02016-8

    Article  Google Scholar 

  8. Zheng L, Guo S (2021) A magnetorheological fluid-based tremor reduction method for robot-assisted catheter operating system. Int J Mechatronics Automation 8(2):72–79. https://doi.org/10.1504/IJMA.2021.115234

    Article  Google Scholar 

  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. Wang H, Wu J, Cai Y (2019) An adaptive deviation-feedback approach for simulating multiple devices interaction in virtual interventional radiology. Comput Aided Des 117:102738. https://doi.org/10.1016/j.cad.2019.102738

    Article  Google Scholar 

  11. Wang Y, Serracino-Inglott F, Yi X, Yuan X-F, Yang X-J (2016) Real-time simulation of catheterization in endovascular surgeries. Comput Animation Virtual Worlds 27(3–4):185–194. https://doi.org/10.1002/cav.1702

    Article  CAS  Google Scholar 

  12. Nowinski WL, Chui C-K (2001) Simulation of interventional neuroradiology procedures. Proceed Int Workshop Med Imaging Augmented Reality, 87–94https://doi.org/10.1109/MIAR.2001.930269

  13. Duriez C, Cotin S, Lenoir J, Neumann P (2006) New approaches to catheter navigation for interventional radiology simulation. Comput Aided Surg 11(6):300–308. https://doi.org/10.3109/10929080601090623

    Article  CAS  PubMed  Google Scholar 

  14. Li S, Guo J, Wang Q, Meng Q, Chui Y, Qin J, Heng P (2012) A catheterization-training simulator based on a fast multigrid solver. IEEE Comput Graphics Appl 32(6):56–70. https://doi.org/10.1109/MCG.2012.32

    Article  Google Scholar 

  15. Li S, Qin J, Guo J, Chui Y-P, Heng P-A (2011) A novel FEM-based numerical solver for interactive catheter simulation in virtual catheterization. Int J Biomed Imaging 2011:815246. https://doi.org/10.1155/2011/815246

    Article  PubMed  PubMed Central  Google Scholar 

  16. Alderliesten T, Bosman PAN, Niessen WJ (2007) Towards a real-time minimally-invasive vascular intervention simulation system. IEEE Trans Med Imaging 26(1):128–132. https://doi.org/10.1109/TMI.2006.886814

    Article  PubMed  Google Scholar 

  17. Alderliesten T, Konings MK, Niessen WJ (2007) Modeling friction, intrinsic curvature, and rotation of guide wires for simulation of minimally invasive vascular interventions. IEEE Trans Biomed Eng 54(1):29–38

    Article  PubMed  Google Scholar 

  18. Luboz V, Blazewski R, Gould D, Bello F (2009) Real-time guidewire simulation in complex vascular models. Vis Comput 25(9):827–834. https://doi.org/10.1007/s00371-009-0312-x

    Article  Google Scholar 

  19. Luboz V, Zhang Y, Johnson S, Song Y, Kilkenny C, Hunt C, Woolnough H, Guediri S, Zhai J, Odetoyinbo T, Littler P, Fisher A, Hughes C, Chalmers N, Kessel D, Clough PJ, Ward J, Phillips R, How T, … Gould D (2013). ImaGiNe Seldinger: First simulator for Seldinger technique and angiography training. Computer Methods and Programs in Biomedicine, 111(2), 419–434https://doi.org/10.1016/j.cmpb.2013.05.014

  20. Alderliesten T, Konings MK, Niessen WJ (2004) Simulation of minimally invasive vascular interventions for training purposes. Comput Aided Surg 9(1–2):3–15. https://doi.org/10.3109/10929080400006408

    Article  PubMed  Google Scholar 

  21. Tran PT, Smoljkic G, Gruijthuijsen C, Reynaerts D, Sloten JV, Poorten EV (2016) Position control of robotic catheters inside the vasculature based on a predictive minimum energy model. 2016 IEEE Int Conference Syst, Man, Cybernetics (SMC), 4687–4693. https://doi.org/10.1109/SMC.2016.7844971

  22. Guo J, Guo S (2017) Design and characteristics evaluation of a novel VR-based robot-assisted catheterization training system with force feedback for vascular interventional surgery. Microsyst Technol 23(8):3107–3116. https://doi.org/10.1007/s00542-016-3086-x

    Article  CAS  Google Scholar 

  23. Gao B, Guo S, Xiao N, Guo J, Xiao X, Yang S, Qu M (2012) Construction of 3D vessel model of the VR robotic catheter system. IEEE Int Conference Inform Automation 2012:783–788. https://doi.org/10.1109/ICInfA.2012.6246925

    Article  Google Scholar 

  24. Guo J, Guo S (2014) A haptic interface design for a VR-based unskilled doctor training system in vascular interventional surgery. IEEE Int Conference Mechatronics Automation 2014:1259–1263. https://doi.org/10.1109/ICMA.2014.6885880

    Article  Google Scholar 

  25. Guo J, Guo S, Tamiya T, Hirata H, Ishihara H (2016) A virtual reality-based method of decreasing transmission time of visual feedback for a tele-operative robotic catheter operating system. Int J Med Robot Comput Assisted Surg 12(1):32–45. https://doi.org/10.1002/rcs.1642

    Article  Google Scholar 

  26. Wang Y, Yang F, Li Y, Yang T, Ren C, Shi Z (2020) A tactile sensation assisted VR catheterization training system for operator’s cognitive skills enhancement. IEEE Access 8:57180–57191. https://doi.org/10.1109/ACCESS.2020.2982219

    Article  Google Scholar 

  27. Yin X, Guo S, Wang Y (2015) Force model-based haptic master console design for teleoperated minimally invasive surgery application. IEEE Int Conference Mechatronics Automation (ICMA) 2015:749–754. https://doi.org/10.1109/ICMA.2015.7237579

    Article  Google Scholar 

  28. Aydin A, Raison N, Khan MS, Dasgupta P, Ahmed K (2016) Simulation-based training and assessment in urological surgery. Nat Rev Urol 13(9):503–519. https://doi.org/10.1038/nrurol.2016.147

    Article  PubMed  Google Scholar 

  29. Bergou M, Wardetzky M, Robinson S, Audoly B, Grinspun E. (2008). Discrete elastic rods. In ACM SIGGRAPH 2008 papers (pp. 1–12).

  30. Shi P, Guo S, Jin X, Li X (2021) Centerline Extraction Method for virtual vascular model in virtual reality interventional training systems. IEEE Int Conference Mechatronics Automation (ICMA) 2021:1060–1064. https://doi.org/10.1109/ICMA52036.2021.9512807

    Article  Google Scholar 

  31. 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  CAS  Google Scholar 

  32. Jin X, Guo S, Guo J, Shi P, Tamiya T, Kawanishi M, Hirata H (2021) Total force analysis and safety enhancing for operating both guidewire and catheter in endovascular surgery. IEEE Sens J 21(20):22499–22509. https://doi.org/10.1109/JSEN.2021.3107188

    Article  Google Scholar 

  33. Zhang L, Guo S, Yu H, Song Y (2017) Performance evaluation of a strain-gauge force sensor for a haptic robot-assisted catheter operating system. Microsyst Technol 23(10):5041–5050. https://doi.org/10.1007/s00542-017-3380-2

    Article  Google Scholar 

  34. Zhang L, Guo S, Yu H, Song Y, Tamiya T, Hirata H, Ishihara H (2018) Design and performance evaluation of collision protection-based safety operation for a haptic robot-assisted catheter operating system. Biomed Microdevice 20(2):22. https://doi.org/10.1007/s10544-018-0266-8

    Article  Google Scholar 

  35. Bao X, Guo S, Xiao N, Li Y, Yang C, Shen R, Cui J, Jiang Y, Liu X, Liu K (2018) Operation evaluation in-human of a novel remote-controlled vascular interventional robot. Biomed Microdevice 20(2):34. https://doi.org/10.1007/s10544-018-0277-5

    Article  CAS  Google Scholar 

  36. Bao X, Guo S, Xiao N, Li Y, Yang C, Jiang Y (2018) A cooperation of catheters and guidewires-based novel remote-controlled vascular interventional robot. Biomed Microdevice 20(1):20. https://doi.org/10.1007/s10544-018-0261-0

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. Kosinski RJ (2008) A literature review on reaction time. Clemson Univ 10(1):337–344

    Google Scholar 

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Acknowledgements

This work was supported in part by the National High-Tech Research and Development Program (863 Program) of China under Grant 2015AA043202, and in part by SPS KAKENHI under Grant 15K2120.

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Authors and Affiliations

  1. School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Peng Shi

  2. Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, the Ministry of Industry and Information Technology, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing, 100081, China

    Shuxiang Guo

  3. Faculty of Engineering and Design, Kagawa University, 2217-20 Hayashi-Cho, Takamatsu, 760-8521, Japan

    Peng Shi, Shuxiang Guo, Xiaoliang Jin & Hideyuki Hirata

  4. State Key Laboratory of Bioelectronics and the Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Xiaoliang Jin

  5. Department of Neurological Surgery, Faculty of Medicine, Kagawa University, Takamatsu, 761-0793, Japan

    Takashi Tamiya & Masahiko Kawanishi

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  1. Peng Shi
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  2. Shuxiang Guo
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Correspondence to Shuxiang Guo.

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Shi, P., Guo, S., Jin, X. et al. A novel catheter interaction simulating method for virtual reality interventional training systems. Med Biol Eng Comput 61, 685–697 (2023). https://doi.org/10.1007/s11517-022-02730-w

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