A study of photoneutron spectra around high-energy medical linear accelerators using Monte Carlo simulations and measurements
Bibliographic record
Abstract
During high-energy radiotherapy treatments, neutrons are produced in the head of the linac through photonuclear interactions. This has been a concern for many years as photoneutrons contribute to the accepted, yet unwanted, out-of-field doses that pose an iatrogenic risk to patients and an occupational risk to personnel. Presently, in-room neutron measurements are difficult and time-consuming and have traditionally been carried out using Bonner spheres with activation foils and TLDs. In this work, a new detector, the Nested Neutron Spectrometer (NNS) is investigated for use in radiotherapy bunkers. It is designed for easy handling and is more practical than the traditional Bonner spheres providing a quicker and more efficient method to measure neutron spectra. Operated in current mode, the NNS was evaluated around a medical linear accelerator at the Montreal General hospital by: determining the performance, comparing with bubble detectors and comparing with Monte Carlo simulations.Firstly, the performance of the NNS was evaluated in high dose-rate environments. Reproducibility, linearity and dose-rate tests showed, with coefficient of variation less than 1%, that the NNS consistently reproduced the same raw measured data in each case. Secondly, equivalent doses measured by bubble detectors were compared with those measured by NNS. Absolute differences ranged from 1% in the treatment room to 50% in the maze. Finally, there was good overall agreement between Monte Carlo simulated and NNS measured spectra at various treatment room locations. Spectral characteristics were similar except for a discrepancy in the peak heights. These tests validate the use of the NNS in radiotherapy.Additionally, the NNS was used to measure neutron spectra around a new linear accelerator operated in flattening filter free (FFF) mode. Our measurements revealed a decrease in total fluence, neutron source strength and equivalent dose of approximately 35 - 40% across the treatment room for measurements in FFF mode compared with those made in flattening filter mode for the same number of MU.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".