Device-Specific Calibration Methods for Optical Frequency Domain Reflectometry-Based Shape Sensing in Catheters and Surgical Needles
Bibliographic record
Abstract
Minimally invasive procedures for diagnosing and treating occlusive arterial diseases and prostate cancer face significant challenges due to the complexity of navigating within occluded arteries and precisely positioning surgical needles. Fiber optic sensors, coupled with optical frequency domain reflectometry (OFDR), offer promising solutions to the accuracy limitations of traditional imaging methods in complex anatomies. This work proposes custom calibration techniques of fiber optic sensors for vascular catheters and prostate surgical needles, addressing device-specific characteristics that can cause shape sensing inaccuracies, making precise and reliable calibration crucial. We assessed how calibration, tool characteristics, and spatial resolution affect shape reconstruction accuracy, with the catheter calibration protocol yielding a root-mean-squared-error (RMSE) of 1.67 ± 0.77 mm (0.4% ± 0.2%), and the needle calibration protocol achieving 0.23 ± 0.14 mm (0.2% ± 0.1%). Although the impact of spatial resolution wasn't significant, it's crucial to consider as it varies with the specific medical device and application.Clinical relevance-The proposed calibration methods enhance the safety and precision of fiber optic minimally invasive procedures by reducing reliance on imaging like fluoroscopy, minimizing tool placement errors across various medical devices and clinical domains. We demonstrate potential for automation to improve both clinical outcomes and workflow efficiency.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".