Development and optimisation of grid inserts for a preclinical radiotherapy system and corresponding Monte Carlo beam simulations
Bibliographic record
Abstract
Abstract Objective . To develop a physical grid collimator compatible with the X-RAD preclinical radiotherapy system and create a corresponding Monte Carlo (MC) model. Approach . This work presents a methodology for the fabrication of a grid collimator designed for utilisation on the X-RAD preclinical radiotherapy system. Additionally, a MC simulation of the grid is developed, which is compatible with the X-RAD treatment planning system. The grid was manufactured by casting a low melting point alloy, cerrobend, into a silicone mould. The silicone was moulded around a 3D-printed replica of the grid, enabling the production of diverging holes with precise radii and spacing. A MC simulation was conducted on an equivalent 3D grid model and validated using 11 layers of GAFChromic EBT-3 film interspersed in a 3D-printed water-equivalent phantom. A 3D dose distribution was constructed from the film layers, enabling a direct comparison with the MC Simulation. Main results . The film and the MC dose distribution demonstrated a gamma passing rate of 99% for a 1%, 0.5 mm criteria with a 10% threshold applied. The peak-to-valley dose ratio and output factor at the surface were determined to be 20.4 and 0.79, respectively. Significance . The pairing of the grid collimator with a MC simulation can significantly enhance the practicality of grid therapy on the X-RAD. This combination enables further exploration of the biological implications of grid therapy, supported by a knowledge of the complex dose distributions. Moreover, this methodology can be adapted for use in other systems and scenarios.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".