Experimental assessment of error in an electromagnetically-tracked ultrasound-guided needle navigation system
Bibliographic record
Abstract
Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".