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Updated: Jan 12, 2026

Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

Three-Dimensional Ultrasonic Needle Tip Tracking with a Fiber-Optic Ultrasound Receiver

Published on: August 21, 2018

Evaluation of Fiber Optic Shape Sensing Models for Minimally Invasive Prostate Needle Procedures Using OFDR Data.

Jacynthe Francoeur¹, Raman Kashyap², Samuel Kadoury³

¹Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA.

IEEE Sensors Journal

|November 7, 2025

Summary

This summary is machine-generated.

Linear Interpolation Models (LIM) offer faster, real-time shape sensing for prostate interventions compared to the Lie-Group Theoretic Model (LGTM), especially in ex vivo tissue, guiding clinical strategy.

Keywords:

Fiber optic shape sensing Lie group methods linear interpolation minimally invasive surgery needle guidance systems optical frequency domain reflectometry (OFDR)prostate needle interventions shape reconstruction algorithms

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Area of Science:

Biomedical Engineering
Medical Robotics
Optical Sensing

Background:

Accurate needle shape sensing is critical for precise prostate interventions.
Fiber optic shape sensing offers a promising solution for real-time tracking.

Purpose of the Study:

To systematically evaluate and compare two fiber optic shape sensing models: Linear Interpolation Models (LIM) and Lie-Group Theoretic Model (LGTM).
To assess model performance across various phantoms and tissue types for prostate needle interventions.

Main Methods:

Utilized a single needle with a three-fiber optical frequency domain reflectometry (OFDR) sensor.
Emulated sparse and quasi-distributed sensing configurations using software-defined strain-point selection.
Evaluated models in gel phantoms, ex vivo tissue, and a cadaveric pig model.

Main Results:

Friedman test showed significant differences in Root Mean Square Errors (RMSEs) between LIM and LGTM (p < 0.05).
LIM outperformed LGTM in ex vivo tissue.
LIM demonstrated over 50-fold faster computation (< 1 ms vs. > 40 ms per shape), enabling real-time application.

Conclusions:

LIM provides a computationally efficient and accurate shape sensing solution for prostate needle interventions.
Model selection involves trade-offs between complexity, sensing density, computational load, and tissue variability.
Findings offer guidance for choosing shape-sensing strategies in clinical and robotic needle-based procedures.