A novel robotic system for vascular intervention: principles, performances, and applications
- 3 Downloads
This paper describes the design, principles, performances, and applications of a novel image-guided master–slave robotic system for vascular intervention (VI), including the performance evaluation and in vivo trials.
Based on the peer-to-peer (P2P) remote communication system, the kinetics analysis, the sliding-mode neural network self-adaptive control model and the feedback system, this new robotic system can accomplish in real time a number of VI operations, including guidewire translation and rotation, balloon catheter translation, and contrast agent injection. The master–slave design prevents surgeons from being exposed to X-ray radiation, which means that they are not required to wear a heavy lead suit. We also conducted a performance evaluation of the new system, which assessed the speed, position tracking, and accuracy, as well as in vivo swine trials.
The speed and position tracking effects are really good, which contribute to the high level of performance in terms of the translational (error ≤ 0.45%) and rotational (error ≤ 2.6°) accuracy. In addition, the accuracy of the contrast agent injection is less than 0.2 ml. The robotic system successfully performed both the stent revascularization of an arteria carotis and four in vivo trials. The haptic feedback data correspond with the robotic-assisted procedure, and peaks and troughs of data occur regularly.
By means of the performance evaluation and four successful in vivo trials, the feasibility and efficiency of the new robotic system are validated, which should prove helpful for further research.
KeywordsVI robotic system Kinetics Sliding-mode neural network Haptic feedback Performance evaluation In vivo swine trial
This work was supported by High Technology Research and Development Program of China (863 Program, No. 2015AA043203), Natural Science Foundation of China (Nos. 61672341, 61471349), The project of Science and Technology Commission of Shanghai municipality (No. 17441903800), The project of major program of National Natural Science Foundation of China (Nos. 61190124, 61190120), Basic Discipline Layout Project of Shenzhen City (No. JCYJ20150731154850923).
Compliance with ethical standards
Conflict of interest
All the authors declare they have no conflict of interest.
The authors declare that all human and animal studies have been approved and performed in accordance with ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 2.World Health Organization (2017) World health statistics 2017: monitoring health for the SDGs. World Health Organization, GenevaGoogle Scholar
- 7.Arai F, Fujimura R, Fukuda T, Negoro M (2002) New catheter driving method using linear stepping mechanism for intravascular neurosurgery. In: IEEE ICRA, 2002. Proceedings, Vol 3, pp 2944–2949Google Scholar
- 9.Guo S, Kondo H, Wang J, Guo J, Tamiya T (2007) A new catheter operating system for medical applications. In: IEEE/ICME, pp 82–86Google Scholar
- 10.Srimathveeravalli G, Kesavadas T, Li X (2010) Design and fabrication of a robotic mechanism for remote steering and positioning of interventional devices. Int J Med Robot Comp 6(2):160–170Google Scholar
- 11.Aoide N, Hansen, Sensei (2012) Hansen medical. Sensei X robotic catheter system. http://dev-hansen.pantheonsite.io/us/en/cardiacarrhythmia/sensei-robotic-system/product-overview
- 12.Romo E, Hart SJ, Covington T (2016) Low friction instrument driver interface for robotic systems [P]. US 20160338783 A1Google Scholar
- 14.Pedersen A (2011) Trial to evaluate CorPath robotic system for PCI. Med Device Daily 15(18):1Google Scholar
- 17.Won JY, Yi BJ, Cha HJ (2016) Review of new master/slave catheter driving vascular intervention robot system: intervention radiologist’s perspective. HMR 36(4):225Google Scholar
- 18.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 Robot Comput Assist Surg Mrcas, 14: e1943Google Scholar
- 19.Zeng Q, Zhou SJ, Shen H, Wang C (2018). A network communication protocols for robotic-assisted vascular intervention systems. BSTGoogle Scholar
- 21.Bai Z (2004) Study of a 3-dof parallel manipulator dynamics based on lagrange’s equation. J B Univ Aeronaut Astronaut 30(1):51–54Google Scholar
- 22.Xia HY, Zhan WB (2003) Application and development expression of lagrange equation. J Luoyang Teach Coll 2:27–29Google Scholar
- 24.Utkin VI, Chang HC (2007) Sliding mode control on electro-mechanical systems. Math Probl Eng 8(4–5):451–473Google Scholar
- 27.Seshagiri S, Khalil HK (2000) Output feedback control of nonlinear systems using RBF neural networks. In: Proceedings of the ACC, 1999, Vol 11, pp 2808–2812Google Scholar
- 28.Welch G, Bishop G (1995). An introduction to the kalman filter. Course Notes 8 of ACM SIGGRAPH 2001, 8(7): 127–132Google Scholar
- 30.Brown RG, Hwang PYC (1997) Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions. Wiley, New YorkGoogle Scholar
- 31.Ovando-Shelley E, Santoyo E, de Oliveir M (2013) Intervention techniques. Geotechnical engineering for the preservation of monuments and historic sites, pp 75–91Google Scholar