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FBG-based Shape-Sensing to Enable Lateral Deflection Methods of Autonomous Needle Insertion.

Dimitri A Lezcano1, Iulian I Iordachita1, Jin Seob Kim1

  • 1Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems
|January 9, 2025
PubMed
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This study enhances needle insertion control for prostate cancer surgery by extending shape-sensing models to manage needle bending. This improves surgical precision and reduces tissue damage during interventions.

Area of Science:

  • Robotics and Control Systems
  • Medical Device Technology
  • Surgical Navigation

Background:

  • Bevel tip needles naturally bend due to asymmetric loading, enabling controlled insertion for precise surgical placement.
  • Autonomous control of needle insertion aims to minimize tissue damage and improve patient outcomes in procedures like prostate cancer diagnosis and treatment.
  • Real-time, intra-operative shape-sensing feedback is crucial for enabling autonomous control methods in needle insertion interventions.

Purpose of the Study:

  • To extend a Lie-group theoretic shape-sensing model to accurately track lateral needle deflection during surgical insertion.
  • To validate the enhanced shape-sensing model using robotic needle insertions in phantom tissue with stereo vision as ground truth.
  • To implement and evaluate a real-time shape-sensing system for autonomous needle insertion control.
Keywords:
autonomous needle insertionfiber Bragg grating (FBG)flexible needlesmulticore fiberneedle shape-sensingreal-time

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Main Methods:

  • Development of an extended Lie-group theoretic model to account for lateral needle deflection.
  • Validation using robotic needle insertion experiments in phantom tissue.
  • Stereo vision system employed for ground truth data acquisition.
  • Implementation of the real-time shape-sensing system using ROS 2.

Main Results:

  • The extended shape-sensing model successfully handles lateral needle deflection during insertion.
  • Robotic insertions in phantom tissue demonstrated the model's efficacy.
  • The real-time system achieved an average feedback frequency of 15 ± 8 Hz.
  • Average needle shape errors were below 1 mm, confirming the model's accuracy.

Conclusions:

  • The extended shape-sensing model accurately tracks needle deflection, enabling enhanced control for surgical interventions.
  • Real-time shape-sensing feedback is feasible and effective for autonomous needle insertion systems.
  • This advancement has the potential to improve precision and reduce invasiveness in procedures requiring accurate needle placement.