A Miniature Optical Neuronavigation System for CT-Guided Stereotaxy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Neuronavigation devices have progressed over the past 2 decades, but logistical limitations remain for many stereotactic procedures. We describe our technique and accuracy for a novel miniature optical tracking system which overcomes these limitations. METHOD: The minioptical tracking system uses a miniature video camera mounted on a rigid cannula to determine cannula location and orientation relative to a patient-attached sticker containing reference markers. A CT scan is used to register these markers to the anatomy and a user-selected target. A computer displays the cannula guidance information to the target. Bench testing was performed on 225 targets in a custom test phantom and additional testing was performed on 20 small targets in an anthropomorphic head phantom to determine the practical accuracy and workflow. RESULTS: The phantom study demonstrated that 3-D navigation accuracy is 1.41 ± 0.53 mm. There was a 100% head phantom study success rate for the 20 small targets. CONCLUSIONS: The resulting accuracy data demonstrated good correlation with the CT data, and the clinical simulation workflow indicated its potential usefulness for common neurosurgical applications. Furthermore, this small-footprint tracking technology does not experience the traditional environmentally induced issues or the requirement of pin-based head fixation, allowing for use in the neurointensive care unit and the emergency department.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".