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Platform for investigating continuum manipulator behavior in orthopedics

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The use of robotic continuum manipulators has been proposed to facilitate less-invasive orthopedic surgical procedures. While tools and strategies have been developed, critical challenges such as system control and intra-operative guidance are under-addressed. Simulation tools can help solve these challenges, but several gaps limit their utility for orthopedic surgical systems, particularly those with continuum manipulators. Herein, a simulation platform which addresses these gaps is presented as a tool to better understand and solve challenges for minimally invasive orthopedic procedures.

Methods

An open-source surgical simulation software package was developed in which a continuum manipulator can interact with any volume model such as to drill bone volumes segmented from a 3D computed tomography (CT) image. Paired simulated X-ray images of the scene can also be generated. As compared to previous works, tool–anatomy interactions use a physics-based approach which leads to more stable behavior and wider procedure applicability. A new method for representing low-level volumetric drilling behavior is also introduced to capture material variability within bone as well as patient-specific properties from a CT.

Results

Similar interaction between a continuum manipulator and phantom bone was also demonstrated between a simulated manipulator and volumetric obstacle models. High-level material- and tool-driven behavior was shown to emerge directly from the improved low-level interactions, rather than by need of manual programming.

Conclusion

This platform is a promising tool for developing and investigating control algorithms for tasks such as curved drilling. The generation of simulated X-ray images that correspond to the scene is useful for developing and validating image guidance models. The improvements to volumetric drilling offer users the ability to better tune behavior for specific tools and procedures and enable research to improve surgical simulation model fidelity. This platform will be used to develop and test control algorithms for image-guided curved drilling procedures in the femur.

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References

  1. Sefati S, Hegeman R, Iordachita I, Taylor RH, Armand M (2021) A dexterous robotic system for autonomous debridement of osteolytic bone lesions in confined spaces: human cadaver studies. IEEE Trans Rob 38(2):1213–1229

    Article  Google Scholar 

  2. Alambeigi F, Wang Y, Sefati S, Gao C, Murphy RJ, Iordachita I, Taylor RH, Khanuja H, Armand M (2017) A curved-drilling approach in core decompression of the femoral head osteonecrosis using a continuum manipulator. IEEE Robot Autom Lett 2(3):1480–1487

    Article  Google Scholar 

  3. Vandini A, Salerno A, Payne CJ, Yang G-Z (2009) Vision-based motion control of a flexible robot for surgical applications. In: 2014 IEEE international conference on robotics and automation (ICRA), pp 6205–6211. IEEE

  4. Wu L, Jaiprakash A, Pandey A, Fontanarosa D, Jonmohamadi Y, Antico M, Strydom M, Razjigaev A, Sasazawa F, Roberts J, et al (2009) Robotic and image-guided knee arthroscopy. Handbook of robotic and image-guided surgery, pp 493–514

  5. Naughton N, Sun J, Tekinalp A, Parthasarathy T, Chowdhary G, Gazzola M (2021) Elastica: a compliant mechanics environment for soft robotic control. IEEE Robot Autom Lett 6(2):3389–3396

    Article  Google Scholar 

  6. Duriez C, Coevoet E, Largilliere F, Morales-Bieze T, Zhang Z, Sanz-Lopez M, Carrez B, Marchal D, Goury O, Dequidt J (2009) Framework for online simulation of soft robots with optimization-based inverse model. In: 2016 IEEE international conference on simulation, modeling, and programming for autonomous robots (SIMPAR), pp 111–118. IEEE

  7. Graule MA, Teeple CB, McCarthy TP, Kim GR, Louis RCS, Wood RJ (2009) Somo: fast and accurate simulations of continuum robots in complex environments. In: 2021 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3934–3941. IEEE

  8. Munawar A, Wang Y, Gondokaryono R, Fischer GS (2009) A real-time dynamic simulator and an associated front-end representation format for simulating complex robots and environments. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1875–1882. IEEE

  9. Munawar A, Li Z, Kunjam P, Nagururu N, Ding AS, Kazanzides P, Looi T, Creighton FX, Taylor RH, Unberath M (2022) Virtual reality for synergistic surgical training and data generation. Comput Methods Biomech Biomed Eng Imaging Vis 10(4):366–374

  10. Gao C, Phalen H, Sefati S, Ma J, Taylor RH, Unberath M, Armand M (2021) Fluoroscopic navigation for a surgical robotic system including a continuum manipulator. IEEE Trans Biomed Eng 69(1):453–464

    Article  PubMed  PubMed Central  Google Scholar 

  11. Quigley M, Conley K, Gerkey B, Faust J, Foote T, Leibs J, Wheeler R, Ng AY, et al (2009) Ros: an open-source robot operating system. In: ICRA workshop on open source software, vol 3, p. 5. Kobe, Japan

  12. Grupp RB, Hegeman RA, Murphy RJ, Alexander CP, Otake Y, McArthur BA, Armand M, Taylor RH (2019) Pose estimation of periacetabular osteotomy fragments with intraoperative X-ray navigation. IEEE Trans Biomed Eng 67(2):441–452

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mirtich BV (1996) Impulse-based dynamic simulation of rigid body systems. PhD thesis, University of California at Berkeley

  14. Schreiber JJ, Anderson PA, Rosas HG, Buchholz AL, Au AG (2011) Hounsfield units for assessing bone mineral density and strength: a tool for osteoporosis management. JBJS 93(11):1057–1063

    Article  Google Scholar 

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Funding

This work was funded in part by NIH R01EB016703 and NIH R01AR080315 and Johns Hopkins University internal funds.

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Authors and Affiliations

  1. Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA

    Henry Phalen, Adnan Munawar, Amit Jain, Russell H. Taylor & Mehran Armand

  2. Department of Orthopaedic Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Amit Jain & Mehran Armand

Authors
  1. Henry Phalen
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  2. Adnan Munawar
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  3. Amit Jain
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  4. Russell H. Taylor
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  5. Mehran Armand
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Corresponding author

Correspondence to Henry Phalen.

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Phalen, H., Munawar, A., Jain, A. et al. Platform for investigating continuum manipulator behavior in orthopedics. Int J CARS 18, 1329–1334 (2023). https://doi.org/10.1007/s11548-023-02945-8

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  • DOI: https://doi.org/10.1007/s11548-023-02945-8

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