Po‐Thur Eve General‐15: Uncertainty of the parallel dose kernel assumption in portal dose prediction with a‐Si electronic portal imagers
Bibliographic record
Abstract
The effect of beam divergence on dose calculation via Monte Carlo generated dose kernels was investigated in an amorphous‐silicon electronic portal imaging device (EPID). The complete detector was simulated in EGSnrc with a 3.0 cm water buildup. The model included details of the detector's imaging cassette, the front cover upstream of it and equivalent backscatter material to approximate the EPID's complex rear housing. Dose kernels were generated with an incident pencil beam of monoenergetic photons of energies 0.1, 2, 6 and 18 MeV. Dose was scored in the phosphor layer of the detector in both cylindrical (at 0° pencil beam orientation) and cartesian (at 0° – 14° in 2° increments) geometries. The parallel (0°) kernels were then convolved with a simple incident fluence map; a full superposition employing the tilted kernels (simulating a divergent beam) and the same incident fluence map was also calculated. Profiles of the dose kernels were observed to demonstrate increasing asymmetry with increasing angle and energy. Comparison of superposition to convolution dose calculation methods in worst‐case‐scenario geometries demonstrated an agreement between the two methods within 0.784 mm distance‐to‐agreement and up to a 1.5% dose difference. More clinically typical field sizes and source‐to‐detector distances were also tested, yielding at most a 1.0% dose difference and the same distance‐to‐agreement. The assumption of parallel dose kernels has less than a 1.5% dosimetric effect in extreme cases and less than a 1.0% dosimetric effect in most clinically relevant situations and should be suitable for most clinical dosimetric applications.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".