Monte Carlo optimization of a GRID collimator for preclinical megavoltage ultra-high dose rate spatially-fractionated radiation therapy
Bibliographic record
Abstract
Abstract Objective . A 2-dimensional pre-clinical SFRT (GRID) collimator was designed for use on the ultra-high dose rate (UHDR) 10 MV ARIEL beamline at TRIUMF. TOPAS Monte Carlo simulations were used to determine optimal collimator geometry with respect to various dosimetric quantities. Approach . The GRID-averaged peak-to-valley dose ratio (PVDR) and mean dose rate of the peaks were investigated with the intent of maximizing both values in a given design. The effects of collimator thickness, focus position, septal width, and hole width on these metrics were found by testing a range of values for each parameter on a cylindrical GRID collimator. For each tested collimator geometry, photon beams with energies of 10, 5, and 1 MV were transported through the collimator and dose rates were calculated at various depths in a water phantom located 1.0 cm from the collimator exit. Main results . In our optimization, hole width proved to be the only collimator parameter which increased both PVDR and peak dose rates. From the optimization results, it was determined that our optimized design would be one which achieves the maximum dose rate for a PVDR ≥ 5 at 10 MV. Ultimately, this was achieved using a collimator with a thickness of 75 mm, 0.8 mm septal and hole widths, and a focus position matched to the beam divergence. This optimized collimator maintained the PVDR of 5 in the phantom between water depths of 0–10 cm at 10 MV and had a mean peak dose rate of 3.06 ± 0.02 Gy s − 1 at 0–1 cm depth. Significance . We have investigated the impact of various GRID-collimator design parameters on the dose rate and spatial fractionation of 10, 5, and 1 MV photon beams. The optimized collimator design for the 10 MV ultra-high dose rate photon beam could become a useful tool for radiobiology studies synergizing the effects of ultra-high dose rate (FLASH) delivery and spatial fractionation.
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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) | 0.002 | 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".