Sci-Thurs PM: Delivery-06: 2-D lag and response nonlinearity corrections for dynamic IMRT verification using an EPID
Bibliographic record
Abstract
In recent years, EPIDs have been used for pre-treatment IMRT verification. Although EPID lag and signal nonlinearities have been investigated, they have not been implemented in the verification process. In dynamic sliding-window IMRT delivery, the dose delivered, and the time between the end of dose delivery and the end of image acquisition differ between pixels. The resulting differences in lag and signal-response across the image can cause artificial asymmetries and amplitude changes in measured EPID dose images. These artifacts alter the agreement between measured and predicted images, potentially complicating the assessment of clinical IMRT verifications. A method of 2-D (pixel-by-pixel) correction was developed based on data from sets of experiments performed to independently quantify the lag and nonlinearity characteristics of Varian's aS500 EPID. To test the correction, it was applied to two sweeping window 10×10 cm2 fields that differ only in sweeping direction. The correction resolved discrepancies in the symmetry between these two cases, and the differences between measured and predicted amplitudes evident when small numbers of MUs were delivered. To illustrate its potential use, the correction technique was applied to a measured image of a clinical IMRT field that produced a relatively poor verification result. The correction partially accounted for discrepancies between measured and Eclipse-predicted images of this field, reducing the percentage of pixels failing a Gamma analysis (3 %, 3 mm) from 8.5 to 5.6 %. This correction technique can be used to help resolve the source of discrepancies in troublesome clinical IMRT verifications.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".