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

This paper presents a systematic evaluation of fiber optic shape sensing models for prostate needle interventions using a single needle embedded with a three-fiber optical frequency domain reflectometry (OFDR) sensor. Two reconstruction algorithms were evaluated: (1) Linear Interpolation Models (LIM), a geometric method that directly estimates local curvature and orientation from distributed strain measurements, and (2) the Lie-Group Theoretic Model (LGTM), a physics-informed elastic-rod model that globally fits curvature profiles while accounting for tissue-needle interaction. Using software-defined strain-point selection, both sparse and quasi-distributed sensing configurations were emulated from the same OFDR data. Experiments were conducted in homogeneous and two-layer gel phantoms, <i>ex vivo</i> tissue, and a whole-body cadaveric pig model. While the repeated-measures ANOVA did not detect any significant differences, the Friedman test analysis revealed statistically significant differences in RMSEs between LIM and LGTM (p < 0.05), with LIM outperforming LGTM in the <i>ex vivo</i> tissue scenario. LIM also achieved over 50-fold faster computation (< 1 ms vs. > 40 ms per shape), enabling real-time use. These findings highlight the trade-offs between model complexity, sensing density, computational load, and tissue variability, providing guidance for selecting shape-sensing strategies in clinical and robotic needle interventions.