<title>2D noise propagation in 3D object position determination from a single-perspective projection</title>
Bibliographic record
Abstract
Image guidance during endovascular intervention is predominantly provided by two-dimensional (2D) digital radiographic systems used for vessel visualization and localization of clips and coils. This paper describes the propagation of 2D noise in the determination of three-dimensional (3D) object position from a single perspective view. In our system, a view is obtained by a digital fluoroscopic x-ray system, corrected for XRII distortions (+/- 0.035mm) and mechanical C-arm shifts (+/- 0.080mm). The tracked object contains high-contrast markers with known relative spacing, allowing for identification and centroid calculation. A least-square projection-Procrustes analysis of the 2D perspective projection is used to determine the 3D position of the object. The effect of uncertainty in 2D marker position on the precision of the 3D object localization using simulations and phantoms was investigated and a nearly linear relationship was found; however, the slope of this relationship is not unity. The slope found indicates a significant amplification of error due to the least-square solution, which is not equally distributed among the 3 major axes. In order to obtain a 3D localization error of less than +/- 1mm, the 2D localization precision must be better than +/- 0.2mm for each marker.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".