Evaluation of Fiber-Optic Shape Sensing Models for Minimally Invasive Prostate Needle Procedures Using OFDR Data
Bibliographic record
Abstract
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, ex vivo 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 (pex vivotissue scenario. LIM also achieved over 50-fold faster computation (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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".