Using multileaf collimator interleaf leakage to extract absolute spatial information from electronic portal imaging device images
Bibliographic record
Abstract
Electronic portal imaging devices (EPIDs) are potentially valuable tools for linear accelerator quality assurance and for measuring and analyzing geometric variations in radiation treatment delivery. Geometric analysis is more robust if referenced against an absolute position such as the isocenter (collimator axis of rotation), allowing the observer to discriminate between various setup errors and jaw or multileaf collimator (MLC) calibration errors. Unfortunately, mechanical instabilities in EPIDs make such analysis difficult. In the present work, we describe how MLC interleaf radiation leakage, hidden in the background of portal images, can be extracted and analyzed to find the field isocenter perpendicular to leaf travel direction. The signal from the interleaf radiation leakage is extracted to provide a precise and accurate determination of the isocenter location in the direction perpendicular to MLC leaf travel. In the direction of leaf travel, the minimization of residuals between planned and measured leaf positions is used to determine the isocenter. This method assumes that leaf positioning errors are randomly distributed. The validity of the method for determining the angular deviation between EPID image grid lines and collimator angle and for determining the known isocenter position is experimentally established.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".