Unified Calibration Technique for Augmented-Reality Ultrasound-Guided Interventions
Bibliographic record
Abstract
Accurate spatial calibration for mobile imaging modality is the essential and enabling technology for augmented-reality based surgical navigation systems. Despite years of research, spatial calibration for surgical camera and freehand ultrasound remains areas of active research. In this paper, we present a unified spatial calibration framework for ultrasound probe calibration and camera hand-eye calibration using the same mathematical principle. By treating spatial calibration as a registration problem between paired points and lines, our framework provides i) efficient solutions with guaranteed convergence properties, and ii) based on error propagation model, a set of heuristic rules for fiducial placements that leads to accurate calibration consistently. Monte Carlo simulation demonstrated that accuracy camera hand-eye calibration (≈ 5 pixel) is possible with the Microsoft HoloLens 2 using as few as 6 fiducial measurements, and accurate ultrasound probe calibration can be consistently obtained using as few as 12 fiducial measurements.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".