Reproducibility of freehand calibrations for ultrasound-guided needle navigation
Bibliographic record
Abstract
PURPOSE: Spatially tracked ultrasound-guided needle insertions may require electromagnetic sensors to be clipped on the needle and ultrasound probe if not already embedded in the tools. It is assumed that switching the electromagnetic sensor clip does not impact the accuracy of the computed calibration. We propose an experimental process to determine whether or not devices should be calibrated on a more frequent basis. METHODS: We performed 250 calibrations. Of these, 125 were performed on the needle and 125 on the ultrasound. Every five calibrations, the tracking clip was removed and reattached. Every 25 calibrations, the tracking clip was exchanged for an identical 3D-printed model. From the resulting transform matrices, coordinate transformations were computed. Data reproducibility was analyzed through looking at the difference between mean and grand mean, standard deviation and the Shapiro-Wilks normality constant. Data was graphically displayed to visualize differences in calibrations in different directions. RESULTS: For the needle calibrations, transformations parallel to the tracking clip and perpendicular to the needle demonstrated the greatest deviation. For the ultrasound calibrations, transformations perpendicular to the sound propagation demonstrated the greatest deviation. CONCLUSION: Needle and ultrasound calibrations are reproducible when changing the tracking clip. These devices do not need to be calibrated on a more frequent basis. Caution should be taken to minimize confounding variables such as bending the needle or ultrasound beam width at the time of calibration. KEY WORDS: Calibration, tracking, reproducibility, electromagnetic, spatial, ultrasound-guided needle navigation, transformation, standard deviation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".