Laser Surface Scanning for Patient Registration in Intracranial Image-guided Surgery
Bibliographic record
Abstract
OBJECTIVE: To report our clinical experience with a new laser scanning-based technique of surface registration. We performed a prospective study to measure the calculated registration error and the application accuracy of laser surface registration for intracranial image-guided surgery in the clinical setting. METHODS: Thirty-four consecutive patients with different intracranial diseases were scheduled for intracranial image-guided surgery by use of a passive infrared surgical navigation system. Surface registration was performed by use of a Class I laser device that emits a visible laser beam. The Polaris camera system (Northern Digital, Waterloo, ON, Canada) detects the skin reflections of the laser, which the software uses to generate a virtual three-dimensional matrix of the anatomy of each patient. An advanced surface-matching algorithm then matches this virtual three-dimensional matrix to the three-dimensional magnetic resonance therapy data set. Registration error as calculated by the computer was noted. Application accuracy was assessed by use of the localization error for three distant anatomic landmarks. RESULTS: Laser surface registration was successful in all patients. For the surgical field, application accuracy was 2.4 +/- 1.7 mm (range, 1-9 mm). Application accuracy was higher for the surgical field of frontally located lesions (mean, 1.8 +/- 0.8 mm; n = 13) as compared with temporal, parietal, occipital, and infratentorial lesions (mean, 2.8 +/- 2.1 mm; n = 21). CONCLUSION: Laser scanning for surface registration is an accurate, robust, and easy-to-use method of patient registration for image-guided surgery.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".