Experimental assessment of error in an electromagnetically-tracked ultrasound-guided needle navigation system
Bibliographic record
Abstract
<strong>Purpose:</strong> Electromagnetic (EM)-tracked ultrasound (US)-guided needle navigation systems have potential use in spinal interventions; however, an assessment of the accuracy of these systems is required. Analysis of these systems involves examining the overall error of the system and the error of its components. The purpose of this study is to estimate the error components in an EM-tracked US-guided needle navigation system, and to determine the relationships between them, specifically for evaluation of US probe calibration. <strong>Methods:</strong> The main parts of the experimental setup are the US probe, the tracker, and the needle. The system error is examined by imaging the tracked needle with the US probe. The positional tracking error is tested for multiple needle, probe and reference sensors using a 7×9 grid with 4 cm spacing between points. Needle calibration error is evaluated by pivot calibration. An upper bound for the probe calibration error is then estimated using a series of transformations between the tracker and the needle tip position. <strong>Results:</strong> For all experiments, the mean error and its standard deviation increase as a function of distance from the tracker. The upper-bound of the US probe calibration error is estimated to be 1.81 mm. <strong>Conclusion:</strong> Operating distance has significant impact on component error, and the optimal operating distance for the presented setup has been shown. Although US probe calibration error cannot be measured directly, its upper-bound has been estimated by assessing the errors in other components of the system.
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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 | 0.001 |
| 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".