Image intensifier distortion correction for fluoroscopic RSA: the need for independent accuracy assessment
Bibliographic record
Abstract
Fluoroscopic images suffer from multiple modes of image distortion. Therefore, the purpose of this study was to compare the effects of correction using a range of two-dimensional polynomials and a global approach. The primary measure of interest was the average error in the distances between four beads of an accuracy phantom, as measured using RSA. Secondary measures of interest were the root mean squared errors of the fit of the chosen polynomial to the grid of beads used for correction, and the errors in the corrected distances between the points of the grid in a second position. Based upon the two-dimensional measures, a polynomial of order three in the axis of correction and two in the perpendicular axis was preferred. However, based upon the RSA reconstruction, a polynomial of order three in the axis of correction and one in the perpendicular axis was preferred. The use of a calibration frame for these three-dimensional applications most likely tempers the effects of distortion. This study suggests that distortion correction should be validated for each of its applications with an independent "gold standard" phantom.
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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 | 0.021 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".