Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-17T23:32:40.225Z Has data issue: false hasContentIssue false

Development and testing of fMRI-compatible haptic interface

Published online by Cambridge University Press:  10 December 2009

Ales Hribar*
Affiliation:
Faculty of Electrical Engineering, Trzaska 25, Ljubljana, Slovenija
Marko Munih
Affiliation:
Faculty of Electrical Engineering, Trzaska 25, Ljubljana, Slovenija
*
*Corresponding author. E-mail: alesh@robo.feuni-lj.si

Summary

This paper presents the development and testing of a haptic interface compatible with a functional magnetic resonance imaging (fMRI) environment for neuroscience human motor control studies. A carbon fiber extension enables us to use the widely accepted and available haptic device Phantom 1.5.

In the first part of the paper development of the mechanical extension together with its kinematic and dynamic models are presented. The second part is focused on testing of the extended haptic interface. The experiment's results both inside and outside the fMRI environment are presented. Tests outside a scanner have shown that the mechanical extension has no notable effect on a subject performance. Experiments with the scanner have confirmed electromagnetic compatibility of the extended haptic system.

At the end it is concluded that the extended haptic device is fully compatible with the fMRI environment, and a virtual environment task that will allow neuroscientists to study a human motor control is proposed.

Type
Article
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Ogawa, S., Menon, R. S., Kim, S. G. and Ugurbil, K., “On the characteristics of functional magnetic resonance imaging of the brain,” Annu. Rev. Biophys. Biomol. Struct. 27, 447474 (1998).CrossRefGoogle ScholarPubMed
2. Lehericy, S., Bardinet, E., Tremblay, L., Van de Moortele, P. F., Pochon, J. B., Dormont, D., Kim, D. S., Yelnik, J. and Ugurbil, K., “Motor control in basal ganglia circuits using fMRI and brain atlas approaches,” Cerebr. Cort. 16, 149161 (2006).CrossRefGoogle ScholarPubMed
3. Toma, K. and Nakai, T., “Functional MRI in human motor control studies and clinical applications,” Magnet. Reson. Med. Sci. 1, 109120 (2002).CrossRefGoogle ScholarPubMed
4. Schueler, B. A., Parrish, T. B., Lin, J. C., Hammer, B. E., Pangrle, B. J., Ritenour, E. R., Kucharczyk, J. and Truwit, C. L., “MRI compatibility and visibility assessment of implantable medical devices,” J. Magnet. Reson. Imag. 9, 596603 (1999).3.0.CO;2-T>CrossRefGoogle ScholarPubMed
5. Chapuis, D., Gassert, R., Sache, L., Burdet, E. and Bleuler, H., “Design of a Simple MRI/fMRI Compatible Force/Torque Sensor,” Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Sep. 28–Oct. 2, 2004).Google Scholar
6. Vogan, J., Wingert, A., Plante, J. S., Dubowsky, S., Hafez, M., Kacher, D. and Jolesz, F., “Manipulation in MRI Devices Using Electrostrictive Polymer Actuators with an Application to Reconfigurable Imaging Coils,” Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans, LA, USA (Apr. 26–May 1, 2004).Google Scholar
7. Chinzei, K. and Miller, K., “MRI Guided Surgical Robot,” Proceedings of the 2001 Australian Conference on Robotics and Automation, Sydney, Australia (Nov. 14–15, 2001).Google Scholar
8. Khanicheh, A., Muto, A., Triantafyllou, C., Weinberg, B., Astrakas, L., Tzika, A. and Mavroidis, C., “fMRI-compatible rehabilitation hand device,” J. NeuroEng. Rehab. 3, 24 (2006).CrossRefGoogle ScholarPubMed
9. Khanicheh, A., Mintzopoulos, D., Weinberg, B., Tzika, A. and Mavroidis, C., “MR_CHIROD v.2: A fMRI Compatible Mechatronic Hand Rehabilitation Device,” Proceedings of the 10th IEEE International Conference on Rehabilitation Robotics, Noordwijk, The Netherlands (Jun. 12–15, 2007).Google Scholar
10. Flueckiger, M., Bullo, M., Chapuis, D., Gassert, R. and Perriard, Y., “fMRI Compatible Haptic Interface Actuated with Traveling Wave Ultrasonic Motor,” Proceedings of the 2005 Industry Applications Conference, 40th IAS Annual Meeting, Vol. 3 (2005) pp. 2075–2082.Google Scholar
11. Yu, N., Murr, W., Blickenstorfer, A., Kollias, S. and Riener, R., “An fMRI Compatible Haptic Interface with Pneumatic Actuation,” Proceedings of the 10th IEEE International Conference on Rehabilitation Robotics, Noordwijk, The Netherlands (Jun. 12–15, 2007).Google Scholar
12. Gassert, R., Moser, R., Burdet, E. and Bleuler, H., “MRI/fMRI-compatible robotic system with force feedback for interaction with human motion,” IEEE/ASME Trans. Mechatron. 11, 216224 (2006).CrossRefGoogle Scholar
13. Yu, N., Hollnagel, C., Blickenstorfer, A., Kollias, S. S. and Riener, R., “Comparison of MRI-compatible mechatronic systems with hydrodynamic and pneumatic actuation,” IEEE/ASME Trans. Mechatron. 13, 268277 (2008).Google Scholar
14. Gassert, R., Dovat, L., Lambercy, O., Ruffieux, Y., Chapuis, D., Ganesh, G., Burdet, E. and Bleuler, H., “A 2-DOF fMRI Compatible Haptic Interface to Investigate the Neural Control of Arm Movements,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, FL, USA (May 15–19, 2006).Google Scholar
15. Izawa, J., Shimizu, T., Aodai, T., Kondo, T., Gomi, H., Toyama, S. and Ito, K., “MR Compatible Manipulandum with Ultrasonic Motor for fMRI Studies,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, FL, USA (May 15–19, 2006).Google Scholar
16. SIEMENS Medical Solutions, “MAGNETIC RESONANCE MAGNETOM Trio: A Tim System,” Technical Drawing (2006).Google Scholar
17. Cavusoglu, M. C. and Feygin, D., “Kinematics and dynamics of Phantom model 1.5 haptic interface,” Presence: Teleop. Virtual Environ. 11 (6), 327343 (2002).Google Scholar
18. Pestel, E. C. and Leckie, F. A., Matrix Methods in Elastomechanics (McGraw-Hill, New York, 1963).Google Scholar
19. Tahmasebi, A. M., Taati, B., Mobasser, F. and Zaad, K. H., “Dynamic Parameter Identification and Analysis of a Phantom Haptic Device”, Proceedings of the 2005 IEEE Conference on Control Applications, Toronto, Canada (Aug. 28–31, 2005).Google Scholar
20. Cavusoglu, M. C., Feygin, D. and Tendick, F., “A critical study of the mechanical and electrical properties of the phantom haptic interface and improvements for high performance control,” Presence: Teleop. Virtual Environ. 11 (5), 555568 (2002).CrossRefGoogle Scholar
21. NEMA, “Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Magnetic Resonance Imaging,” Standards Publication MS 1-2001, (May 8, 2008). Available at http://www.nema.org/stds/ms1.cfm#downloadGoogle Scholar
22. Harwin, W. and Hillman, M., “Introduction,” Robotica 21 (1), 1 (2003).CrossRefGoogle Scholar
23. Krebs, H. I., Volpe, B. T., Aisen, M. L., Hening, W., Adamovich, S., Poizner, H., Subrahmanyan, K. and Hogan, N., “Robotic applications in neuromotor rehabilitation,” Robotica 21 (1), 311 (2003).CrossRefGoogle Scholar