Poster — Thur Eve — 27: Scattering foil redesign for modulated electron radiotherapy
Bibliographic record
Abstract
This work serves to build on existing work that discussed the advantages of removing the scattering foil from the beamline for modulated electron radiotherapy (MERT) much like flattening filter-free beams in photon IMRT. Due to concerns about the ability of the accelerators transmission ionization chamber to function normally with narrow, minimally-scattered beams as well as a limited maximum field size, this study focused on the design of a new custom scattering foil that was optimized for MERT applications. Different foil parameters such as material, thickness and shape were investigated to produce a minimally scattered beam, as opposed to a flat beam in the case of the clinical foil, while preserving the dosimetric benefits of a reduced thickness foil such as dramatically higher dose rate and reduced photon contamination in the bremsstrahlung tail. Using a Monte Carlo model that was verified against measured data, fluence profiles at the level of the transmission ionization chamber were produced and analyzed for all foil configurations, as well as PDDs and profiles in a water phantom. It was found that a simple foil of slab geometry made of low to medium Z material with the thickness determined by the maximum desirable field size and chosen electron energy produced a sufficiently scattered beam that maintained the dosimetric advantages of a scattering-foil-free beamline. These new foils, when applied to MERT, have the advantage of reducing treatment time due to higher dose rates and reducing dose to healthy tissue due to reduced dose in the bremsstrahlung tail.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".