Advertisement

Annals of Biomedical Engineering

, Volume 46, Issue 10, pp 1650–1662 | Cite as

Toward Semi-autonomous Cryoablation of Kidney Tumors via Model-Independent Deformable Tissue Manipulation Technique

  • Farshid Alambeigi
  • Zerui Wang
  • Yun-hui Liu
  • Russell H. Taylor
  • Mehran Armand
Medical Robotics

Abstract

We present a novel semi-autonomous clinician-in-the-loop strategy to perform the laparoscopic cryoablation of small kidney tumors. To this end, we introduce a model-independent bimanual tissue manipulation technique. In this method, instead of controlling the robot, which inserts and steers the needle in the deformable tissue (DT), the cryoprobe is introduced to the tissue after accurate manipulation of a target point on the DT to the desired predefined insertion location of the probe. This technique can potentially reduce the risk of kidney fracture, which occurs due to the incorrect insertion of the probe within the kidney. The main challenge of this technique, however, is the unknown deformation behavior of the tissue during its manipulation. To tackle this issue, we proposed a novel real-time deformation estimation method and a vision-based optimization framework, which do not require prior knowledge about the tissue deformation and the intrinsic/extrinsic parameters of the vision system. To evaluate the performance of the proposed method using the da Vinci Research Kit, we performed experiments on a deformable phantom and an ex vivo lamb kidney and evaluated our method using novel manipulability measures. Experiments demonstrated successful real-time estimation of the deformation behavior of these DTs while manipulating them to the desired insertion location(s).

Keywords

Deformable tissue manipulation Autonomous manipulation Robot-assisted laparoscopic cryoablation Model-independent manipulation 

Notes

Acknowledgments

This work is supported in part by the NIH/NIBIB Grant R01EB016703, by the HK RGC under Grants 415011, by the HK ITF under Grants ITS/112/15FP and ITT/012/15GP, by the project #BME-8115053 of the Shun Hing Institute of Advanced Engineering, CUHK, and by the project 4930745 of the CUHK T Stone Robotics Institute, CUHK.

Supplementary material

Supplementary material 1 (MP4 10947 kb)

References

  1. 1.
    Azar, F. S., D. N. Metaxas, and M. D. Schnall. Methods for modeling and predicting mechanical deformations of the breast under external perturbations. Med. Image Anal. 6:1–27, 2002.CrossRefGoogle Scholar
  2. 2.
    Baker, S., and I. Mathews. Lucas-Kanade Years On: A Unifying Framework: Part 1, 2 & 3. Pittsburgh: Robotics Institute, Carnegie Mellon University, 2004.Google Scholar
  3. 3.
    Bradski, G., and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. Sebastopol: O’Reilly Media, Inc., 2008.Google Scholar
  4. 4.
    Broyden, C. G. A class of methods for solving nonlinear simultaneous equations. Math. Comput. 19:577–593, 1965.CrossRefGoogle Scholar
  5. 5.
    Cestari, A., G. Guazzoni, V. Dell’acqua, L. Nava, G. Cardone, G. Balconi, R. Naspro, F. Montorsi, and P. Rigatti. Laparoscopic cryoablation of solid renal masses: intermediate term followup. J. Urol. 172:1267–1270, 2004.CrossRefGoogle Scholar
  6. 6.
    Chen A. I., M. L. Balter, T. J. Maguire and M. L. Yarmush. Real-time needle steering in response to rolling vein deformation by a 9-DOF image-guided autonomous venipuncture robot. In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, IEEE, pp. 2633–2638, 2015.Google Scholar
  7. 7.
    Fadda M., D. Bertelli, S. Martelli, M. Marcacci, P. Dario, C. Paggetti, D. Caramella and D. Trippi. Computer assisted planning for total knee arthroplasty. In: CVRMed-MRCAS’97. Springer, pp. 617–628, 1997.Google Scholar
  8. 8.
    Finley, D. S., S. Beck, G. Box, W. Chu, L. Deane, D. J. Vajgrt, E. M. McDougall, and R. V. Clayman. Percutaneous and laparoscopic cryoablation of small renal masses. J. Urol. 180:492–498, 2008.CrossRefGoogle Scholar
  9. 9.
    Gill, P. E., W. Murray, and M. H. Wright. Practical Optimization. London: Academic Press, 1981.Google Scholar
  10. 10.
    Glauser, D., H. Fankhauser, M. Epitaux, J.-L. Hefti, and A. Jaccottet. Neurosurgical robot Minerva: first results and current developments. J. Image Guid. Surg. 1:266–272, 1995.CrossRefGoogle Scholar
  11. 11.
    Goksel, O., E. Dehghan, and S. E. Salcudean. Modeling and simulation of flexible needles. Med. Eng. Phys. 31:1069–1078, 2009.CrossRefGoogle Scholar
  12. 12.
    Kazanzides P., Z. Chen, A. Deguet, G. S. Fischer, R. H. Taylor and S. P. DiMaio. An open-source research kit for the da Vinci® Surgical System. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on. IEEE, pp. 6434–6439, 2014.Google Scholar
  13. 13.
    Khadem, M., C. Rossa, R. S. Sloboda, N. Usmani, and M. Tavakoli. Ultrasound-guided model predictive control of needle steering in biological tissue. J. Med. Robot. Res. 1:1640007, 2016.CrossRefGoogle Scholar
  14. 14.
    Khadem, M., C. Rossa, N. Usmani, R. S. Sloboda, and M. Tavakoli. Semi-automated needle steering in biological tissue using an ultrasound-based deflection predictor. Ann. Biomed. Eng. 45:924–938, 2017.CrossRefGoogle Scholar
  15. 15.
    Misra, S., K. B. Reed, B. W. Schafer, K. Ramesh, and A. M. Okamura. Mechanics of flexible needles robotically steered through soft tissue. Int. J. Robot. Res. 29:1640–1660, 2010.CrossRefGoogle Scholar
  16. 16.
    Moustris, G., S. Hiridis, K. Deliparaschos, and K. Konstantinidis. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int. J. Med. Robot. Comput. Assist. Surg. 7:375–392, 2011.CrossRefGoogle Scholar
  17. 17.
    Quigley M., K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng. ROS: an open-source Robot Operating System. In: ICRA workshop on open source software, Kobe, p. 5, 2009.Google Scholar
  18. 18.
    Shademan, A., R. S. Decker, J. D. Opfermann, S. Leonard, A. Krieger, and P. C. Kim. Supervised autonomous robotic soft tissue surgery. Sci. Transl. Med. 8:337ra364–337ra364, 2016.CrossRefGoogle Scholar
  19. 19.
    Webster, III, R. J., J. S. Kim, N. J. Cowan, G. S. Chirikjian, and A. M. Okamura. Nonholonomic modeling of needle steering. Int. J. Robot. Res. 25:509–525, 2006.CrossRefGoogle Scholar
  20. 20.
    Wright, S. J., and J. Nocedal. Numerical optimization. Springer Sci 35:7, 1999.Google Scholar
  21. 21.
    Yoshikawa, T. Manipulability of robotic mechanisms. Int. J. Robot. Res. 4:3–9, 1985.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  1. 1.Laboratory for Computational Sensing and RoboticsJohns Hopkins UniversityBaltimoreUSA
  2. 2.The Department of Mechanical and Automation Engineering, T Stone Robotics InstituteThe Chinese University of Hong KongShatinHong Kong
  3. 3.Johns Hopkins University Applied Physics LaboratoryLaurelUSA

Personalised recommendations