Redundant System of Passive Markers for Ultrasound Scanhead Tracking
Bibliographic record
Abstract
Scanhead tracking by opto-electronic (OE) systems allows high accuracy in three-dimensional (3-D) freehand ultrasound imaging. In this paper, a new set of methods is proposed and compared with the standard approach [Gram-Schmidt method (GS)]. Three redundancy-based algorithms are introduced to compensate for possible loss of markers during data acquisition: regression plane (RP), multiple Gram-Schmidt (MGS), and center of mass least square (CMLS). When combined with the ultrasound instrument, the root-mean-squared (RMS) uncertainty in locating target points, over a working volume of 420 mm x 490 mm x 100 mm, improved by 7% and 24% using MGS and CMLS method respectively, compared to GS. A lower improvement was obtained with RP methods (5%), using the best marker configuration. In conclusion, CMLS method provides a robust and accurate procedure for 3-D freehand ultrasound scanhead tracking, able to manage possible loss of markers, with interesting perspectives for image fusion and body referenced 3-D ultrasound.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".