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Robotic laser osteotomy through penscriptive structured light visual servoing

  • Jamil JivrajEmail author
  • Ryan Deorajh
  • Phillips Lai
  • Chaoliang Chen
  • Nhu Nguyen
  • Joel Ramjist
  • Victor X. D. Yang
Original Article
  • 9 Downloads

Abstract

Purpose

Planning osteotomies is a task that surgeons do as part of standard surgical workflow. This task, however, becomes more difficult and less intuitive when a robot is tasked with performing the osteotomy. In this study, we aim to provide a new method for surgeons to allow for highly intuitive trajectory planning, similar to the way an attending surgeon would instruct a junior.

Methods

Planning an osteotomy, especially during a craniotomy, is performed intraoperatively using a sterile surgical pen or pencil directly on the exposed bone surface. This paper presents a new method for generating osteotomy trajectories for a multi-DOF robotic manipulator using the same method and relaying the penscribed cut path to the manipulator as a three-dimensional trajectory. The penscribed cut path is acquired using structured light imaging, and detection, segmentation, optimization and orientation generation of the Cartesian trajectory are done autonomously after minimal user input.

Results

A 7-DOF manipulator (KUKA IIWA) is able to follow fully penscribed trajectories with sub-millimeter accuracy in the target plane and perpendicular to it (0.46 mm and 0.36 mm absolute mean error, respectively).

Conclusions

The robot is able to precisely follow cut paths drawn by the surgeon directly onto the exposed boney surface of the skull. We demonstrate through this study that current surgical workflow does not have to be drastically modified to introduce robotic technology in the operating room. We show that it is possible to guide a robot to perform an osteotomy in much the same way a senior surgeon would show a trainee by using a simple surgical pen or pencil.

Keywords

Laser osteotomy Trajectory generation Drilling Penscription Planning Structured light Image processing Feature detection 

Notes

Acknowledgements

Thank you to Jillian Cardinell for the graphics help.

Funding

Funding for this research was provided by The Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant: “Optical Coherence Tomography, Optical Topographical Imaging and Fluorescence Guided Surgical Laser Ablation”—Grant Number RGPIN/6263-2014.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Humans and animal rights

This article does not contain and studies with human or animal participants performed by any of the authors. All ex-vivo animal parts were obtained in accordance with the research ethics guidelines of Ryerson University. All patient data were anonymized in accordance with Sunnybrook Health Sciences Centre policy. Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2019

Authors and Affiliations

  • Jamil Jivraj
    • 1
    Email author
  • Ryan Deorajh
    • 1
  • Phillips Lai
    • 1
  • Chaoliang Chen
    • 1
  • Nhu Nguyen
    • 1
  • Joel Ramjist
    • 1
  • Victor X. D. Yang
    • 1
    • 2
  1. 1.Biophotonics & Bioengineering Laboratory, Department of Electrical & Computer EngineeringRyerson UniversityTorontoCanada
  2. 2.Division of Neurosurgery, Sunnybrook Health Sciences CentreTorontoCanada

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