Po‐Poster ‐ 18: Investigation of tilted dose kernels for portal dose prediction in a‐Si electronic portal imagers
Bibliographic record
Abstract
The effect of beam divergence on dose calculation in an amorphous silicon electronic portal imaging device (EPID) was investigated with Monte Carlo generated dose kernels. The flat‐panel detector was simulated in EGSnrc (user code DOSXYZnrc) with a 3.0 cm water buildup; the model included details of the detector's imaging cassette and the front cover upstream of it. To approximate the effect of the EPID's rear housing, a 21 mm air gap and 10 mm water slab were introduced into the simulation as equivalent backscatter material. Kernels were generated with monoenergetic 2, 6, and 18 MeV photons with the orientation of the pencil beam varying from 0 to 14 degrees in 2 degree increments. Dose was scored in the phosphor layer of the detector. To reduce statistical fluctuations at large radial distances from the incident pencil beam, the kernels were first averaged bilaterally and then combined into square half rings. Profiles of the kernels were observed to demonstrate increasing asymmetry with increasing angle and energy, while the total energy deposited in the phosphor by the 2 MeV pencil beam decreased by greater than 2% at larger angles. Further investigation via comparison of superposition to convolution dose calculation methods is required to determine the effect these angled kernels have on calculation accuracy in clinical beam geometry.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".