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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
Summary
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.
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.
Keywords:
autonomous needle insertionfiber Bragg grating (FBG)flexible needlesmulticore fiberneedle shape-sensingreal-time
