A two-step algorithm for predicting portal dose images in arbitrary detectors
Bibliographic record
Abstract
Recently, portal imaging systems have been successfully demonstrated in dosimetric treatment verification applications, where measured and predicted images are quantitatively compared. To advance this approach to dosimetric verification, a two-step model which predicts dose deposition in arbitrary portal image detectors is presented. The algorithm requires patient CT data, source-detector distance, and knowledge of the incident beam fluence. The first step predicts the fluence entering a portal imaging detector located behind the patient. Primary fluence is obtained through ray-tracing techniques, while scatter fluence prediction requires a library of Monte Carlo-generated scatter fluence kernels. These kernels allow prediction of basic radiation transport parameters characterizing the scattered photons, including fluence and mean energy. The second step of the algorithm involves a superposition of Monte Carlo-generated pencil beam kernels, describing dose deposition in a specific detector, with the predicted incident fluence. This process is performed separately for primary and scatter fluence, and yields a predicted dose image. A small but noticeable improvement in prediction is obtained by explicitly modeling the off-axis energy spectrum softening due to the flattening filter. The algorithm is tested on a slab phantom and a simple lung phantom (6 MV). Furthermore, an anthropomorphic phantom is utilized for a simulated lung treatment (6 MV), and simulated pelvis treatment (23 MV). Data were collected over a range of air gaps (10-80 cm). Detectors incorporating both low and high atomic number buildup are used to measure portal image profiles. Agreement between predicted and measured portal dose is better than 3% in areas of low dose gradient (<30%/cm) for all phantoms, air gaps, beam energies, and detector configurations tested here. It is concluded that this portal dose prediction algorithm is fast, accurate, allows separation of primary and scatter dose, and can model arbitrary detectors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".