Sci‐PM Fri ‐ 09: Image analysis of inter‐leaf radiation leakage, a new approach to the correction of EPID mechanic inconsistencies
Bibliographic record
Abstract
Electronic portal imaging devices are valuable for analyzing patient treatment data for geometric errors. The analysis can be more robust if an absolute reference position such as the isocentre (collimator axis of rotation) is used, allowing discrimination between setup errors and Jaw/MLC calibration errors. Conventionally, investigators have extracted field edges through global thresholding, and compared the treated to the planned field placement using techniques such as the method of moments and normalized cross‐correlation. The accuracy of these methods depends on the accuracy and consistency of the MLC and Jaw calibration as well as on EPID performance such as intensity uniformity. In this work we describe how the MLC inter‐leaf radiation leakage, hidden in the background of portal images, can be extracted and analyzed to find the field isocentre. The peak locations of inter‐leaf radiation leakage in a portal image are extracted providing a very precise and accurate determination of the isocentre location in the direction perpendicular to the MLC leaf travel. Clearly orthogonal portal images can provide a very accurate determination of beam isocentre in both directions. The orientation of the interleaf leakage can also be used to determine mechanical inconsistencies in the EPID structure as a function of gantry angle. The image analysis of the inter‐leaf radiation leakage for determining imager translation and orientation is described.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".