Interactive Multi-Stage Robotic Positioner for Intra-Operative MRI-Guided Stereotactic Neurosurgery.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date:

Abstract

Magnetic resonance imaging (MRI) demonstrates clear advantages over other imaging modalities in neurosurgery with its ability to delineate critical neurovascular structures and cancerous tissue in high-resolution 3D anatomical roadmaps. However, its application has been limited to interventions performed based on static pre/post-operative imaging, where errors accrue from stereotactic frame setup, image registration, and brain shift. To leverage the powerful intra-operative functions of MRI, e.g., instrument tracking, monitoring of physiological changes and tissue temperature in MRI-guided bilateral stereotactic neurosurgery, a multi-stage robotic positioner is proposed. The system positions cannula/needle instruments using a lightweight (203 g) and compact (Ø97 × 81 mm) skull-mounted structure that fits within most standard imaging head coils. With optimized design in soft robotics, the system operates in two stages: i) manual coarse adjustment performed interactively by the surgeon (workspace of ±30°), ii) automatic fine adjustment with precise (<0.2° orientation error), responsive (1.4 Hz bandwidth), and high-resolution (0.058°) soft robotic positioning. Orientation locking provides sufficient transmission stiffness (4.07 N/mm) for instrument advancement. The system's clinical workflow and accuracy is validated with lab-based (<0.8 mm) and MRI-based testing on skull phantoms (<1.7 mm) and a cadaver subject (<2.2 mm). Custom-made wireless omni-directional tracking markers facilitated robot registration under MRI.

Authors

  • Zhuoliang He
    Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
  • Jing Dai
    National Center for Occupational Safety and Health, Beijing 102308, China.
  • Justin Di-Lang Ho
    Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
  • Hon-Sing Tong
    Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
  • Xiaomei Wang
    The Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong.
  • Ge Fang
    The Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong.
  • Liyuan Liang
    Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China.
  • Chim-Lee Cheung
    Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
  • Ziyan Guo
    Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.
  • Hing-Chiu Chang
    Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China.
  • Iulian Iordachita
    Laboratory for Computational Sensing and Robotics (LCSR), The Johns Hopkins University, Baltimore, MD, USA.
  • Russell H Taylor
    Johns Hopkins University, Baltimore, MD, USA.
  • Wai-Sang Poon
    Division of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, 999077, China.
  • Danny Tat-Ming Chan
    Division of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, 999077, China.
  • Ka-Wai Kwok
    Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.