Detector dose response in megavoltage small photon beams. II. Pencil beam perturbation effects
Bibliographic record
Abstract
PURPOSE: To quantify detector perturbation effects in megavoltage small photon fields and support the theoretical explanation on the nature of quality correction factors in these conditions. METHODS: In this second paper, a modern approach to radiation dosimetry is defined for any detector and applied to small photon fields. Fano's theorem is adapted in the form of a cavity theory and applied in the context of nonstandard beams to express four main effects in the form of perturbation factors. The pencil-beam decomposition method is detailed and adapted to the calculation of perturbation factors and quality correction factors. The approach defines a perturbation function which, for a given field size or beam modulation, entirely determines these dosimetric factors. Monte Carlo calculations are performed in different cavity sizes for different detection materials, electron densities, and extracameral components. RESULTS: Perturbation effects are detailed with calculated perturbation functions, showing the relative magnitude of the effects as well as the geometrical extent to which collimating or modulating the beam impacts the dosimetric factors. The existence of a perturbation zone around the detector cavity is demonstrated and the approach is discussed and linked to previous approaches in the literature to determine critical field sizes. CONCLUSIONS: Monte Carlo simulations are valuable to describe pencil beam perturbation effects and detail the nature of dosimetric factors in megavoltage small photon fields. In practice, it is shown that dosimetric factors could be avoided if the field size remains larger than the detector perturbation zone. However, given a detector and beam quality, a full account for the detector geometry is necessary to determine critical field sizes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".