Assessment of the Accuracy of Optical Shape Sensing for Needle Tracking Interventions
Bibliographic record
Abstract
Accurate needle guidance is essential for a number of magnetic resonance imaging (MRI)-guided percutaneous procedures, such as radiofrequency ablation (RFA) of metastatic liver tumors. A promising technology to obtain real-time tracking of the shape and tip of a needle is by using high-frequency (up to 20 kHz) fiber Bragg grating (FBG) sensors embedded in optical fibers, which are insensitive to external magnetic fields. We fabricated an MRI-compatible needle designed for percutaneous procedures with a series of FBG sensors which would be tracked in an image-guidance system, allowing to display the needle shape within a navigation image. A series of phantom experiments demonstrated needle tip tracking errors of 1.05 ± 0.08 mm for a needle deflection up to 16.82 mm on a ground-truth model and showed nearly similar accuracy to electromagnetic (EM) tracking (i.e., 0.89 ± 0.09 mm). We demonstrated feasibility of the FBG-based tracking system for MRI-guided interventions with differences under 1 mm between tracking systems. This study establishes the needle tracking accuracy of FBG needle tracking for image-guided procedures.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".