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Related Experiment Video

Updated: Jul 29, 2025

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Lie-Group Theoretic Approach to Shape-Sensing Using FBG-Sensorized Needles Including Double-Layer Tissue and S-Shape

Dimitri A Lezcano1, Iulian I Iordachita1, Jin Seob Kim1

  • 1Mechanical Engineering Department, Johns Hopkins University, MD 21201 USA.

IEEE Sensors Journal
|May 22, 2023
PubMed
Summary

This study validates a new 3D shape-sensing method for flexible surgical needles using fiber Bragg grating (FBG) sensors. The validated model accurately predicts complex needle curvatures during insertion, improving minimally-invasive surgery precision.

Keywords:
Lie-groupfiber opticsfiber-Bragg gratingflexible needlemedical deviceshape-sensing

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Area of Science:

  • Medical Engineering
  • Robotics
  • Surgical Navigation

Background:

  • Flexible needles are crucial for minimally-invasive surgery, enabling navigation in complex anatomical spaces.
  • Accurate intra-operative needle localization is essential for safe and effective procedures, often requiring radiation-based imaging.
  • Existing shape-sensing models may not fully capture the complex curvatures of flexible needles.

Purpose of the Study:

  • To validate a theoretical model for 3D shape-sensing of flexible needles with complex curvatures.
  • To extend a previous sensor-based model by integrating fiber Bragg grating (FBG) sensor data and elastic rod mechanics.
  • To assess the model's accuracy in predicting needle shape during insertion in various tissue types and configurations.

Main Methods:

  • Developed a model combining FBG sensor curvature measurements and inextensible elastic rod mechanics.
  • Evaluated the model in C- and S-shape insertions in single-layer isotropic tissue.
  • Tested the model in C-shape insertions in two-layer isotropic tissue with varying stiffnesses.
  • Used stereo vision for 3D ground truth needle shape determination.

Main Results:

  • The model successfully predicted 3D needle shapes for complex curvatures.
  • Mean needle shape-sensing root-mean-square errors were 0.160 ± 0.055 mm.
  • Validation was performed over 650 needle insertions across different tissue properties and insertion scenarios.

Conclusions:

  • The validated theoretical method provides accurate 3D shape-sensing for flexible needles.
  • This approach enables precise needle placement in minimally-invasive surgery without relying on radiation.
  • The model accounts for complex curvatures, enhancing surgical navigation and patient safety.