Body-mounted MR-conditional Robot for Minimally Invasive Liver Intervention
Zhefeng Huang, Anthony L. Gunderman, Samuel E. Wilcox, Saikat Sengupta, Jay Shah, Aiming Lu, David Woodrum, Yue Chen
TL;DR
Aims to improve MR-guided liver ablation by enabling precise needle guidance inside closed-bore MRI with a body-mounted robot. The system employs two stacked 2-DoF Cartesian stages to provide 4 active DoFs for the needle, driven by MR-safe pneumatic turbines, and controlled via a surgeon-in-loop strategy. Results show axis-position errors around $0.18$–$0.19$ mm and needle-tip targeting errors near $2.6$–$3$ mm in free-space and phantom tests, with MRI phantom validation indicating negligible imaging degradation. The work demonstrates MR-conditional operation in a standard MRI bore and potential workflow improvements for MR-guided ablation, while identifying limitations such as a $30^{\circ}$ insertion incline and compressor-driven actuation, pointing to future optimization and in vivo validation.
Abstract
MR-guided microwave ablation (MWA) has proven effective in treating hepatocellular carcinoma (HCC) with small-sized tumors, but the state-of-the-art technique suffers from sub-optimal workflow due to speed and accuracy of needle placement. This paper presents a compact body-mounted MR-conditional robot that can operate in closed-bore MR scanners for accurate needle guidance. The robotic platform consists of two stacked Cartesian XY stages, each with two degrees of freedom, that facilitate needle guidance. The robot is actuated using 3D-printed pneumatic turbines with MR-conditional bevel gear transmission systems. Pneumatic valves and control mechatronics are located inside the MRI control room and are connected to the robot with pneumatic transmission lines and optical fibers. Free space experiments indicated robot-assisted needle insertion error of 2.6$\pm$1.3 mm at an insertion depth of 80 mm. The MR-guided phantom studies were conducted to verify the MR-conditionality and targeting performance of the robot. Future work will focus on the system optimization and validations in animal trials.
