Monte Carlo optimization of a microbeam collimator design for use on the small animal radiation research platform (SARRP)
Bibliographic record
Abstract
Abstract Microbeam radiation therapy (MRT) is a pre-clinical, spatially-fractionated treatment modality noted for its ability to achieve a large differential response between normal and tumoral tissues. In the present study, TOPAS Monte Carlo (MC) simulations were used to optimize the design of a compact, affordable multi-slit collimator (MSC) suitable for use with the small animal radiation research platform (SARRP). MRT dose distributions in a (1 × 1 × 3)cm 3 water phantom were simulated for a tungsten MSC using different focal spot sizes (0.4, 3 mm), beam energies (40, 80, 220 kVp), slit widths (100, 125, 150, 175, 200 µ m), collimator thicknesses (1.5, 2.5, 3 cm) and collimator-to-surface distances (CSD of 1 and 3 cm). Key MRT figures of merit, namely the peak-to-valley dose ratio (PVDR), full-width at half-maximum and peak dose rate were determined. Use of the small focal spot maximized the PVDR (~40 at surface) and reduced the system’s sensitivity to changes in CSD, but decreased the collimated beam output to 55.2 cGy min −1 . The large focal spot was ill-suited for large CSD irradiations, but increased the beam output by a factor of 2.8, to 153.0 cGy min −1 , and decreased the sensitivity to changes in slit width. A modular MSC, using divergent plastic spacer materials in place of excavated slits, was also investigated. Polypropylene and polyethylene terephthalate material spacers were considered and while neither reduced the PVDR compared to air slits, the dose rate was reduced by 37% and 47%, respectively. Lastly, a steel parallel-slit MSC was used in a preliminary test of MRT delivery using the SARRP. Discrepancies between the results of film dosimetry and the corresponding MC simulations highlight the need to fabricate a more well-defined collimator for use in future validation and radiobiological work. The simulated results of this study are being used to inform the design of such a collimator, which will additionally boast a high degree of modularity at reasonable cost.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".