SU‐E‐T‐194: Accounting for Support Arm Backscatter in EPID‐Based IMRT QA
Bibliographic record
Abstract
Purpose: To devise a method for IMRT QA using a Pinnacle model for the EPID response which accounts for non‐uniform backscatter from the support arm. Method: The method is based on a Pinnacle treatment planning system model. It relies on determining a correction matrix (CM) that multiplies the (flood‐field calibrated) EPID image to restore pixel response to backscatter and subsequently constructing a backscatter region within the Pinnacle model that reproduces the effect of backscatter from the EPID support arm. The CM is constructed from a series of profiles calculated as the ratio of modelled to acquired slit‐beam fields. Each slit beam is offset so that the series covers the entire EPID area. Slit beams are used to minimize the dependence of response to backscatter. The CM represents the flood‐field response of the EPID mounted on the support arm as if it had been calibrated in the absence of backscatter. An iterative fitting procedure is used to adjust the backscatter region in the Pinnacle model so that the modelled flood‐field response matches the CM. Results: As expected, arbitrary‐field EPID images multiplied by the CM agree well with Pinnacle model results incorporating the backscatter region. The method was tested for 10, 20, and 30 cm square fields and shown to reduce discrepancies between the Pinnacle calculation and EPID images from 3.3, 8.3 and 9.4 %, to 0.8, 2.2 and 1.1 % respectively for the three fields. Conclusions: A novel method for IMRT QA was developed whereby acquired EPID images are multiplied by a pre‐computed correction matrix and then compared to the response from a Pinnacle model which incorporates a non‐uniform backscatter region. The method is easy to use in clinical practice and does not require measurements involving the removal of the EPID from the support arm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".