Medical linear accelerator mounted mini-beam collimator: design, fabrication and dosimetric characterization
Bibliographic record
Abstract
The goal of this work was to design, build and experimentally characterize a linear accelerator mounted mini-beam collimator for use at a nominal 6 MV beam energy. Monte Carlo simulation was used in the design and dosimetric characterization of a compact mini-beam collimator assembly mounted to a medical linear accelerator. After fabrication, experimental mini-beam dose profiles and central axis relative output were measured and the results used to validate the simulation data. The simulation data was then used to establish traceability back to an established dosimetric code of practice. The Monte Carlo simulation work revealed that changes in collimator blade width have a greater influence on the valley-to-peak dose ratio than do changes in blade height. There was good agreement between the modeled and measured profile data, with the exception of small differences on either side of the central peak dose. These differences were found to be systematic across all depths and result from limitations associated with the collimator fabrication. Experimental mini-beam relative output and simulation data agreed to better than ± 2.0%, which is well within the level of uncertainty required for dosimetric traceability of non-standard field geometries. A mini-beam collimator has now been designed, built and experimentally characterized for use with a commercial linear accelerator operated at a nominal 6 MV beam energy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".