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Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches

Medical Physics 2023-09-11 v1 Robotics

Abstract

Flexible needle insertion procedures are common for minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively. Through expansive research in fiber optics, a plethora of bio-compatible, MRI-compatible, optical shape-sensors have been developed to provide real-time shape feedback, such as single-core and multicore fiber Bragg gratings. In this paper, we directly compare single-core fiber-based and multicore fiber-based needle shape-sensing through identically constructed, four-active area sensorized bevel-tip needles inserted into phantom and \exvivo tissue on the same experimental platform. In this work, we found that for shape-sensing in phantom tissue, the two needles performed identically with a pp-value of 0.164>0.050.164 > 0.05, but in \exvivo real tissue, the single-core fiber sensorized needle significantly outperformed the multicore fiber configuration with a pp-value of 0.0005<0.050.0005 < 0.05. This paper also presents the experimental platform and method for directly comparing these optical shape sensors for the needle shape-sensing task, as well as provides direction, insight and required considerations for future work in constructively optimizing sensorized needles.

Keywords

Cite

@article{arxiv.2309.04407,
  title  = {Optical Fiber-Based Needle Shape Sensing in Real Tissue: Single Core vs. Multicore Approaches},
  author = {Dimitri A. Lezcano and Yernar Zhetpissov and Alexandra Cheng and Jin Seob Kim and Iulian I. Iordachita},
  journal= {arXiv preprint arXiv:2309.04407},
  year   = {2023}
}
R2 v1 2026-06-28T12:16:25.264Z