Polymer-based binary robots and mechatronics devices can lead to simple, robust, and cost effective solutions for Magnetic Resonnace Image-guided (MRI) medical procedures. A binary manipulator using 12 elastically averaged air muscles has been proposed for MRI-guided biopsies and brachytherapies procedures used for prostate cancer diagnostic and treatment. In this design, radially-distributed air muscles position a needle guide relatively to the MRI table. The system constitutes an active compliant mechanism where the compliance relieves the over-constraint imposed by the redundant parallel architecture. This paper presents experimental results for repeatability, accuracy, and stiffness of a fully functional manipulator prototype. Results show an experimental repeatability of 0.1 mm for point-to-point manipulation on a workspace diameter of 80 mm. Manipulator average accuracy is 4.7 mm when based on the nominal (uncalibrated) model and improves to 2.1 mm when using a calibrated model. The estimated stiffness at the end-effector is ∼0.95 N/mm and is sufficient to withstand the needle insertion forces without major deflection. Needle trajectories during state change appear to be primarily driven by the system’s elastic energy gradient. The study shows the manipulator prototype to meet its design criteria and to have the potential of becoming an effective and low-cost manipulator for MRI-guided prostate cancer treatment.
Skip Nav Destination
ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 15–18, 2010
Montreal, Quebec, Canada
Conference Sponsors:
- Design Engineering Division and Computers in Engineering Division
ISBN:
978-0-7918-4410-6
PROCEEDINGS PAPER
Experimental Validation of an Elastically Averaged Binary Manipulator for MRI-Guided Prostate Cancer Interventions
Sylvain Proulx,
Sylvain Proulx
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Search for other works by this author on:
Genevie`ve Miron,
Genevie`ve Miron
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Search for other works by this author on:
Alexandre Girard,
Alexandre Girard
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Search for other works by this author on:
Jean-Se´bastien Plante
Jean-Se´bastien Plante
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Search for other works by this author on:
Sylvain Proulx
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Genevie`ve Miron
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Alexandre Girard
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Jean-Se´bastien Plante
Universite´ de Sherbrooke, Sherbrooke, QC, Canada
Paper No:
DETC2010-28235, pp. 409-418; 10 pages
Published Online:
March 8, 2011
Citation
Proulx, S, Miron, G, Girard, A, & Plante, J. "Experimental Validation of an Elastically Averaged Binary Manipulator for MRI-Guided Prostate Cancer Interventions." Proceedings of the ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 34th Annual Mechanisms and Robotics Conference, Parts A and B. Montreal, Quebec, Canada. August 15–18, 2010. pp. 409-418. ASME. https://doi.org/10.1115/DETC2010-28235
Download citation file:
16
Views
Related Proceedings Papers
Related Articles
Design and Experimental Assessment of an Elastically Averaged Binary Manipulator Using Pneumatic Air Muscles for Magnetic Resonance Imaging Guided Prostate Interventions
J. Mech. Des (November,2011)
An MRI-Compatible Needle Manipulator Concept Based on Elastically Averaged Dielectric Elastomer Actuators for Prostate Cancer Treatment: An Accuracy and MR-Compatibility Evaluation in Phantoms
J. Med. Devices (September,2009)
An Integrated Probe for Magnetic Resonance Imaging Monitored Skin Cryosurgery
J Biomech Eng (February,1995)
Related Chapters
Using Statistical Learning Theory to Improve Treatment Response for Metastatic Colorectal Carcinoma
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
Performance Validation Using Several Statistical Learning Theory Paradigms for Mammogram Screen Film and Clinical Data Features
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Biomolecular Feature Selection of Colorectal Cancer Microarray Data Using GA-SVM Hybrid
Intelligent Engineering Systems through Artificial Neural Networks