Comparison between an in‐house 1D profile correction method and a 2D correction provided in Varian's PDPC Package for improving the accuracy of portal dosimetry images
Bibliographic record
Abstract
While commissioning Varian's Portal Dose Image Prediction (PDIP) algorithm for portal dosimetry, an asymmetric radial response in the portal imager due to backscatter from the support arm was observed. This asymmetric response led to differences on the order of 2%-3% for simple square fields (< 20 × 20 cm2) when comparing the measured to predicted portal fluences. A separate problem was that discrepancies of up to 10% were seen in measured to predicted portal fluences at increasing off-axis distance (> 10 cm). We have modified suggested methods from the literature to provide a 1D correction for the off-axis response problem which adjusts the diagonal profile used in the portal imager calibration. This inherently cannot fix the 2D problem since the PDIP algorithm assumes a radially symmetric response and will lead to some uncertainty in portal dosimetry results. Varian has recently released generic "2D correction" files with their Portal Dosimetry Pre-configuration (PDPC) package, but no independent testing has been published. We present the comparison between QA results using the Varian correction method to results using our 1D profile correction method using the gamma passing rates with a 3%, 3 mm criterion. The average, minimum, and maximum gamma pass rates for nine fixed-field IMRT fields at gantry 0° using our profile correction method were 98.1%, 93.7%, and 99.8%, respectively, while the results using the PDPC correction method were 98.4%, 93.1%, and 99.8%. For four RapidArc fields, the average, minimum, and maximum gamma pass rates using our correction method were 99.6%, 99.4%, and 99.9%, respectively, while the results using the PDPC correction method were 99.8%, 99.5%, and 99.9%. The average gamma pass rates for both correction methods are quite similar, but both show improvement over the uncorrected results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".