Application of a model‐based water‐equivalent EPID image conversion algorithm for linac beam QA
Bibliographic record
Abstract
PURPOSE: The nonwater-equivalent energy response of electronic portal imaging devices (EPIDs) is a major obstacle to using them for linear accelerator (linac) beam parameter verification. In this study, we propose an EPID-based machine quality assurance (QA) application that uses a model-based radiation transport algorithm to convert EPID-measured images into water-equivalent dose distributions that can be used to assess beam flatness and symmetry. METHODS: An in-house developed, model-based radiation transport algorithm was used to estimate the incident beam fluence from measured EPID images and convert it into either 3D dose distributions in a virtual water tank or 2D water-equivalent dose distributions in a virtual ion chamber array. The conversion algorithm was validated using independent measurements in a scanning water tank and a reference ion chamber array under symmetric and also intentionally detuned (i.e., asymmetric) beam conditions. RESULTS: For symmetric fields, EPID-reconstructed percentage depth dose distributions (PDDs) agreed with water tank measurements to within 1% beyond the first 10 mm of depth. Beam profile comparisons showed differences within 1% in low dose-gradient regions. For all symmetric and intentionally asymmetric fields, beam flatness and symmetry derived from reconstructed images agreed with reference measurements to within 0.2% and 0.3%, respectively. The model demonstrated high sensitivity to the controlled beam asymmetries and steering distortions, with EPID-reconstructed metrics closely matching reference water-equivalent measurements and significantly outperforming metrics derived from raw EPID images. CONCLUSIONS: The proposed model-based algorithm enables accurate conversion of EPID images into water-equivalent dose distributions, facilitating accurate determination of beam flatness and symmetry. This application addresses some limitations of the previously proposed EPID-based linac QA techniques, which are limited to nonwater-equivalent constancy checks, and supports the use of EPIDs as robust dosimetry tools for linac radiation beam parameter verification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".