Design of an MR-compatible biopsy needle manipulator using pull-pull cable transmission

  • Seong-Tae Kim
  • Yeongjin Kim
  • Jung KimEmail author


Magnetic resonance (MR) compatible needle manipulators can assist physicians with the insertion of biopsy needles and needle-like therapeutic instruments directly into millimeter-size tumors, using MR images as feedback. However, magnetic resonance imaging (MRI) systems present a challenging operational environment with high magnetic fields and limited space, making the development of MR-compatible robots difficult. In this paper, we present design requirement analysis and a novel prototype design for an MRIguided biopsy needle manipulator and an MR-compatible transmission that provides sufficient driving forces for needle manipulation inside an MR scanner. The actuators of the manipulator are placed outside the MR room, which are several meters away from the scanner. A combination of a pull-pull cable-sheath and a cable-pulley transmission is used to transmit the actuator force to the manipulator. This configuration can be used to achieve MR-compatibility and leads to the ability to use conventional actuators such as DC motors, which have sufficiently high power to perform needle biopsy. We also studied the feasibility of the proposed transmission and actuation with the proposed manipulator. The accuracy and precision of the needle tip are evaluated and MRcompatibility is verified.


MR-compatible Biopsy needle manipulator Pull-pull cable transmission Cable-sheath transmission 



rolling angle of the manipulator


pitching angle of the manipulator


sliding displacement of the manipulator


sin θ i


cos θ i


ith joint axis (J 1: roll, J 2: pitch, J 3: slide)

x, y, z

x, y and z-directional position of the needle tip with respect to base coordinate system, respectively


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kim, Y. H. and Le Minh, H., “A Laboratory-Level Surgical Robot System for Minimal Invasive Surgery (MIS) Total Knee Arthroplasty,” Int. J. Precis. Eng. Manuf., Vol. 12, No. 2, pp. 237–242, 2011.CrossRefGoogle Scholar
  2. 2.
    Na, Y., Seo, J.-M., and Kim, J., “Electromagnetic Tracking of Needle Intervention for Sacral Nerve Stimulation using the Image-Guided Surgery Toolkit (IGSTK),” Int. J. Precis. Eng. Manuf., Vol. 14, No. 11, pp. 2015–2020, 2013.CrossRefGoogle Scholar
  3. 3.
    Elhawary, H., Tse, Z. T. H., Hamed, A., Rea, M., Davies, B. L., and Lamperth, M. U., “The Case for MRCompatible Robotics: A Review of the State of the Art,” The International Journal of Medical Robotics and Computer Assisted Surgery, Vol. 4, No. 2, pp. 105–113, 2008.CrossRefGoogle Scholar
  4. 4.
    Gassert, R., Burdet, E., and Chinzei, K., “Opportunities and Challenges in MR-Compatible Robotics,” IEEE Engineering in Medicine and Biology Magazine, Vol. 27, No. 3, pp. 15–22, 2008.CrossRefGoogle Scholar
  5. 5.
    Gassert, R., Yamamoto, A., Chapuis, D., Dovat, L., Bleuler, H., and Burdet, E., “Actuation Methods for Applications in MREnvironments,” Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering, Vol. 29, No. 4, pp. 191–209, 2006.CrossRefGoogle Scholar
  6. 6.
    Gassert, R., Moser, R., Burdet, E., and Bleuler, H., “MRI/fMRICompatible Robotic System with Force Feedback for Interaction with Human Motion,” IEEE Asme Transactions on Mechatronics, Vol. 11, No. 2, pp. 216–224, 2006.CrossRefGoogle Scholar
  7. 7.
    Masamune, K., Kobayashi, E., Masutani, Y., Suzuki, M., Dohi, T., et al., “Development of an MRI-Compatible Needle Insertion Manipulator for Stereotactic Neurosurgery,” Journal of Image Guided Surgery, Vol. 1, No. 4, pp. 242–248, 1995.CrossRefGoogle Scholar
  8. 8.
    Suzumori, K., Hashimoto, T., Uzuka, K., and Enomoto, I., “Pneumatic Direct-Drive Stepping Motor for Robots,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2031–2036, 2002.CrossRefGoogle Scholar
  9. 9.
    Stoianovici, D., Patriciu, A., Petrisor, D., Mazilu, D., and Kavoussi, L., “A New Type of Motor: Pneumatic Step Motor,” IEEE/ASME Transactions on Mechatronics, Vol. 12, No. 1, pp. 98–106, 2007.CrossRefGoogle Scholar
  10. 10.
    Stoianovici, D., Kim, C., Srimathveeravalli, G., Sebrecht, P., Petrisor, D., et al., “MRI-Safe Robot for Endorectal Prostate Biopsy,” IEEE/ASME Transactions on Mechatronics, Vol. 19, No. 4, pp. 1289–1299, 2014.CrossRefGoogle Scholar
  11. 11.
    Chapuis, D., Gassert, R., Ganesh, G., Burdet, E., and Bleuler, H., “Investigation of a Cable Transmission for the Actuation of MRCompatible Haptic Interfaces,” Proc. of the 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 426–431, 2006.Google Scholar
  12. 12.
    Menon, S., Brantner, G., Aholt, C., Kay, K., and Khatib, O., “Haptic FMRI: Combining Functional Neuroimaging with Haptics for Studying the Brain's Motor Control Representation,” Proc. of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4137–4142, 2013.Google Scholar
  13. 13.
    Vigaru, B., Sulzer, J., and Gassert, R., “Design and Evaluation of a Cable-Driven fMRI-Compatible Haptic Interface to Investigate Precision Grip Control,” IEEE Transactions on Haptics, Vol. 9, No. 1, pp. 20–32, 2016.CrossRefGoogle Scholar
  14. 14.
    General Electric Company, “Optima MR450w, Optima 450w GEM, Preinstallation Manual,” (Accessed 22 JUL 2016)Google Scholar
  15. 15.
    Silva, M., “Microfocus X-ray Sources,” (Accessed 5 JUL 2016)Google Scholar
  16. 16.
    Wu, Q., Wang, X., Chen, L., and Du, F., “Transmission Model and Compensation Control of Double-Tendon-Sheath Actuation System,” IEEE Transactions on Industrial Electronics, Vol. 62, No. 3, pp. 1599–1609, 2015.CrossRefGoogle Scholar
  17. 17.
    DiMaio, S. P., Pieper, S., Chinzei, K., Hata, N., Haker, S., et al., “Robot-Assisted Needle Placement in Open MRI: System Architecture, Integration and Validation,” Computer Aided Surgery, Vol. 12, No. 1, pp. 15–24, 2007.CrossRefGoogle Scholar
  18. 18.
    Krieger, A., Song, S.-E., Cho, N. B., Iordachita, I. I., Guion, P., et al., “Development and Evaluation of an Actuated MRI-Compatible Robotic System for MRI-Guided Prostate Intervention,” IEEE/ASME Transactions on Mechatronics, Vol. 18, No. 1, pp. 273–284, 2013.CrossRefGoogle Scholar
  19. 19.
    Wallner, K., Blasko, J., and Dattoli, M. J., “Prostate Brachytherapy: Made Complicated,” SmartMedicine Press, 2nd Ed., 2001.Google Scholar
  20. 20.
    Bak, J. B., Landas, S. K., and Haas, G. P., “Characterization of Prostate Cancer Missed by Sextant Biopsy,” Clinical Prostate Cancer, Vol. 2, No. 2, pp. 115–118, 2003.CrossRefGoogle Scholar
  21. 21.
    Krieger, A., Iordachita, I. I., Guion, P., Singh, A. K., Kaushal, A., et al., “An MRI-Compatible Robotic System with Hybrid Tracking for MRI-Guided Prostate Intervention,” IEEE Transactions on Biomedical Engineering, Vol. 58, No. 11, pp. 3049–3060, 2011.CrossRefGoogle Scholar
  22. 22.
    Schenck, J. F., “The Role of Magnetic Susceptibility in Magnetic Resonance Imaging: MRI Magnetic Compatibility of the First and Second Kinds,” Medical Physics, Vol. 23, No. 6, pp. 815–850, 1996.CrossRefGoogle Scholar
  23. 23.
    Victrex, “Victrex Peek Polymers,” (Accessed 5 JUL 2016)Google Scholar
  24. 24.
    Plasticsintl, “TIVAR 1000, Ultra High Molecular Weight Polyethylene, UHMW-PE,” (Accessed 5 JUL 2016)Google Scholar
  25. 25.
    Stoianovici, D., “Multi-Imager Compatible Actuation Principles in Surgical Robotics,” The International Journal of Medical Robotics and Computer Assisted Surgery, Vol. 1, No. 2, pp. 86–100, 2005.CrossRefGoogle Scholar
  26. 26.
    Yu, N., Gassert, R., and Riener, R., “Mutual Interferences and Design Principles for Mechatronic Devices in Magnetic Resonance Imaging,” International Journal of Computer Assisted Radiology and Surgery, Vol. 6, No. 4, pp. 473–488, 2011.CrossRefGoogle Scholar
  27. 27.
    Hsia, T. C. and Gao, L. S., “Robot Manipulator Control using Decentralized Linear Time-Invariant Time-Delayed Joint Controllers,” Proc. of IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2070–2075, 1990.CrossRefGoogle Scholar
  28. 28.
    National Electrical Manufactures Association, “Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Magnetic Resonance Imaging,” Document ID: 100123, 2008.Google Scholar
  29. 29.
    Firbank, M. J., Coulthard, A., Harrison, R. M., and Williams, E., “A Comparison of Two Methods For Measuring the Signal to Noise Ratio on MRImages,” Physics in Medicine and Biology, Vol. 44, No. 12, pp. N261–N264, 1999.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringKAISTDaejeonSouth Korea
  2. 2.Department of Mechanical EngineeringIncheon National UniversityIncheonSouth Korea

Personalised recommendations