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Proceedings Paper

A standardized method for accuracy study of MRI-compatible robots: case study: a body-mounted robot
Author(s): E. Siampli; R. Monfaredi; S. Pieper; Pan Li; V. Beskin; K. Cleary
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Paper Abstract

Here we report on a phantom targeting study for accuracy evaluation of our body-mounted robot for Magnetic Resonance Imaging (MRI) guided arthrography. We use a standardized method developed in a multi-institute effort with the aim of providing an objective method for accuracy and signal-to-noise Ratio (SNR) evaluation of MRI-compatible robots. The medical definition of arthrography is the radiographic visualization of a joint (as the hip or shoulder) after the injection of a radiopaque substance. That procedure provides an evaluation of the joints using two medical imaging modalities, fluoroscopic x-ray imaging and MRI. Conventional arthrography is done in two stages: first the contrast dye injected into the joint (fluoroscopic procedure) and then an MRI to evaluate the joint space. Our MRI-guided compatible robot is intended to enable needle placement in the MRI environment, streamlining the procedure. The targeting study was conducted using the quality assessment mockup phantom and associated software called QARAI that was developed by the URobotics Laboratory at Johns Hopkins and colleagues. The mockup contains four embedded fiducials and an 8 by 8 grid which is used to automatically identify the targeting points with high accuracy. The study was conducted on a Philips Achieva 1.5T MRI system and 10 points were targeted. All targets were reached with an average error of 2.71mm. The targeting algorithm, as well as the control of the robot, were completed using robot control modules developed with the open source software 3D Slicer.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152T (16 March 2020); doi: 10.1117/12.2550575
Show Author Affiliations
E. Siampli, Children's National Health System (United States)
R. Monfaredi, Children's National Health System (United States)
S. Pieper, Isomics, Inc. (United States)
Pan Li, Tianjin Univ. of Science and Technology (China)
V. Beskin, Children's National Health System (United States)
K. Cleary, Children's National Health System (United States)

Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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