Advertisement

Computer-assisted planning for a concentric tube robotic system in neurosurgery

  • Josephine GrannaEmail author
  • Arya Nabavi
  • Jessica Burgner-Kahrs
Original Article

Abstract

Purpose

Laser-induced thermotherapy in the brain is a minimally invasive procedure to denature tumor tissue. However, irregularly shaped brain tumors cannot be treated using existing commercial systems. Thus, we present a new concept for laser-induced thermotherapy using a concentric tube robotic system. The planning procedure is complex and consists of the optimal distribution of thermal laser ablations within a volume as well as design and configuration parameter optimization of the concentric tube robot.

Methods

We propose a novel computer-assisted planning procedure that decomposes the problem into task- and robot-specific planning and uses a multi-objective particle swarm optimization algorithm with variable length.

Results

The algorithm determines a Pareto-front of optimal ablation distributions for three patient datasets. It considers multiple objectives and determines optimal robot parameters for multiple trajectories to access the tumor volume.

Conclusions

We prove the effectiveness of our planning procedure to enable the treatment of irregularly shaped brain tumors. Multiple trajectories further increase the applicability of the procedure.

Keywords

Minimally invasive surgery Concentric tube robot Planning Neurosurgery Robotic-surgery 

Notes

Funding

This research was supported in parts by the International Neurobionics Foundation and by the German Research Foundation under Award No. BU-2935/1-1.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Altrogge I, Preusser T, Kröger T, Büskens C, Pereira P, Schmidt D, Peitgen HO (2007) Multiscale optimization of the probe placement for radiofrequency ablation. Acad Radiol 14:1310–24Google Scholar
  2. 2.
    Audigier C, Mansi T, Delingette H, Rapaka S, Passerini T, Mihalef V, Jolly MP, Pop R, Diana M, Soler L, Kamen A, Comaniciu D, Ayache N (2017) Comprehensive preclinical evaluation of a multi-physics model of liver tumor radiofrequency ablation. Int J Comput Assist Radiol Surg 12(9):1543–1559Google Scholar
  3. 3.
    Baegert C, Villard C, Schreck P, Soler L (2007) Multi-criteria trajectory planning for hepatic radiofrequency ablation. In: Medical image computing and computer-assisted intervention—MICCAI, pp 676–684Google Scholar
  4. 4.
    Baykal C, Torres LG, Alterovitz R (2015) Optimizing design parameters for sets of concentric tube robots using sampling-based motion planning. In: IEEE international conference on intelligent robots and systems, pp 4381–4387Google Scholar
  5. 5.
    Bedell C, Lock J, Gosline AH, Dupont PE (2012) Design optimization of concentric tube robots based on task and anatomical constraints. In: IEEE international conference on robotics and automation, pp 398–403Google Scholar
  6. 6.
    Bergeles C, Gosline AH, Vasilyev NV, Codd PJ, del Nido PJ, Dupont PE (2015) Concentric tube robot design and optimization based on task and anatomical constraints. IEEE Trans Robot 31(1):67–84Google Scholar
  7. 7.
    Burdette EC, Rucker DC, Prakash P, Diederich CJ, Croom JM, Clarke C, Stolka P, Juang T, Boctor EM, Webster III RJ (2010) The ACUSITT ultrasonic ablator: the first steerable needle with an integrated interventional tool. In: SPIE medical imaging, pp 1–10Google Scholar
  8. 8.
    Burgner J, Gilbert HB, Webster III RJ (2013) On the computational design of concentric tube robots: incorporating volume-based objectives. In: IEEE international conference on robotics and automation, pp 1193–1198Google Scholar
  9. 9.
    Burgner-Kahrs J, Rucker DC, Choset H (2015) Continuum robots for medical applications: a survey. IEEE Trans Robot 31(6):1261–1280Google Scholar
  10. 10.
    Chen CCR, Miga MI, Galloway RL (2009) Optimizing electrode placement using finite-element models in radiofrequency ablation treatment planning. IEEE Trans Biomed Eng 56(2):237–245Google Scholar
  11. 11.
    Comber DB, Slightam JE, Gervasi VR, Neimat JS, Barth EJ (2016) Design, additive manufacture, and control of a pneumatic MR-compatible needle driver. IEEE Trans Robot 32(1):138–149Google Scholar
  12. 12.
    Delorme M, Iori M, Martello S (2015) Bin packing and cutting stock problems: mathematical models and exact algorithms. Eur J Oper Res 255(1):1–20Google Scholar
  13. 13.
    Granna J, Graf A, Nabavi A, Burgner-Kahrs J (2017) A manual actuation system for laser induced thermal therapy of malignant brain tumors. In: Proceedings of the annual meeting of the german society for computer- and robot-assisted surgery, pp 125–130Google Scholar
  14. 14.
    Granna J, Nabavi A, Burgner-Kahrs J (2017) Toward computer-assisted planning for interstitial laser ablation of malignant brain tumors using a tubular continuum robot. In: Medical image computing and computer-assisted intervention—MICCAI, pp 557–565Google Scholar
  15. 15.
    Graves C, Slocum A, Gupta R, Walsh CJ (2012) Towards a compact robotically steerable thermal ablation probe. In: IEEE international conference on robotics and automation, pp 709–714Google Scholar
  16. 16.
    Ha J, Park FC, Dupont PE (2014) Achieving elastic stability of concentric tube robots through optimization of tube precurvature. In: IEEE/RSJ international conference on intelligent robots and systems, pp 864–870Google Scholar
  17. 17.
    Kahrs LA, Burgner J, Klenzner T, Raczkowsky J, Schipper J, Wörn H (2010) Planning and simulation of microsurgical laser bone ablation. Int J Comput Assist Radiol Surg 5(2):155–162Google Scholar
  18. 18.
    Kapoor A, Li M, Wood B (2011) Mixed variable optimization for radio frequency ablation planning. In: SPIE medical imaging, pp 1–7Google Scholar
  19. 19.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948Google Scholar
  20. 20.
    Li G, Su H, Cole GA, Shang W, Harrington K, Camilo A, Pilitsis JG, Fischer GS (2015) Robotic system for MRI-guided stereotactic neurosurgery. IEEE Trans Biomed Eng 62(4):1077–1088Google Scholar
  21. 21.
    McCreedy ES, Cheng R, Hemler PF, Viswanathan A, Wood BJ, McAuliffe MJ (2006) Radio frequency ablation registration, segmentation, and fusion tool. IEEE Trans Inf Technol Biomed 10(3):490–496Google Scholar
  22. 22.
    Mensel B, Weigel C, Hosten N (2006) Laser-induced thermotherapy. Recent Res Cancer Res 167:69–75Google Scholar
  23. 23.
    Motkoski JW, Yang FW, Lwu SHH, Sutherland GR (2013) Toward robot-assisted neurosurgical lasers. IEEE Trans Biomed Eng 60(4):892–898Google Scholar
  24. 24.
    Mukhopadhyay A, Mandal M (2014) Identifying non-redundant gene markers from microarray data: a multiobjective variable length PSO-based approach. IEEE Trans Comput Biol Bioinform 11(6):1545–5963Google Scholar
  25. 25.
    Ren H, Campos-Nanez E, Yaniv Z, Banovac F, Abeledo H, Hata N, Cleary K (2014) Treatment planning and image guidance for radiofrequency ablation of large tumors. IEEE J Biomed Health Inf 18(3):920–928Google Scholar
  26. 26.
    Ren H, Guo W, Sam Ge S, Lim W (2014) Coverage planning in computer-assisted ablation based on genetic algorithm. Comput Biol Med 49(1):36–45Google Scholar
  27. 27.
    Rezapour M, Leuthardt E, Gorlewicz LJ (2016) Design of a steerable guide for laser interstitial thermal therapy of brain tumors. J Med Dev 10(3):1–2Google Scholar
  28. 28.
    Su B, Tang J, Liao H (2015) Automatic laser ablation control algorithm for an novel endoscopic laser ablation end effector for precision neurosurgery. In: IEEE international conference on intelligent robots and systems, pp 4362–4367Google Scholar
  29. 29.
    Swaney PJ, Burgner J, Pheiffer TS, Rucker DC, Gilbert HB, Ondrake JE, Simpson AL, Burdette EC, Miga MI, Webster III RJ (2012) Tracked 3D ultrasound targeting with an active Cannula. In: SPIE medical imaging, pp 1–9Google Scholar
  30. 30.
    Tani S, Tatli S, Hata N, Garcia-Rojas X, Olubiyi OI, Silverman SG, Tokuda J (2016) Three-dimensional quantitative assessment of ablation margins based on registration of pre- and post-procedural MRI and distance map. Int J Comput Assist Radiol Surg 11(6):1133–1142Google Scholar
  31. 31.
    Xue B, Ma X, Wang H, Gu J, Li Y (2014) Improved variable-length particle swarm optimization for structure-adjustable extreme learning machine. Control Intell Syst 42(4):1–9Google Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Laboratory for Continuum RoboticsLeibniz Universität HannoverHanoverGermany
  2. 2.International Neuroscience Institute, Image Guided Neurosurgical TherapyHanoverGermany

Personalised recommendations