The geometric calibration of cone–beam systems with arbitrary geometry
Bibliographic record
Abstract
A method is proposed to determine the cone-beam x-ray acquisition geometry of an imaging system using a phantom consisting of discrete x-ray opaque markers defining two parallel rings sharing a common axis. The phantom generates an image of two ellipses which are fitted to an ellipse model. A phantom-centric coordinate system is used to simplify the equations describing the ellipse coefficients such that a solution describing the acquisition geometry can be obtained via numerical optimization of only three of the nine unknown variables. We perform simulations to show how errors in the fit of the ellipse coefficients affect estimates of the acquisition geometries. These simulations show that for ellipse projections sampled with 1200 markers, 25 microm errors in marker positions and a source-detector distance (SDD) of 1.6 m, we can measure angles describing detector rotation with a mean error of <0.002 degrees and a standard deviation (SD) of <0.03 degrees. The SDD has a mean error of 0.004 mm and SD = 0.24 mm. The largest error is associated with the determination of the point on the detector closest to the x-ray source (mean error = 0.05 mm, SD = 0.85 mm). A prototype phantom was built and results from x-ray experiments are presented.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".