Mini-GRID radiotherapy on the CLEAR very-high-energy electron beamline: collimator optimization, film dosimetry, and Monte Carlo simulations
Bibliographic record
Abstract
Abstract Objective. Spatially-fractionated radiotherapy (SFRT) delivered with a very-high-energy electron (VHEE) beam and a mini-GRID collimator was investigated to achieve synergistic normal tissue-sparing through spatial fractionation and the FLASH effect. Approach. A tungsten mini-GRID collimator for delivering VHEE SFRT was optimized using Monte Carlo (MC) simulations. Peak-to-valley dose ratios (PVDRs), depths of convergence (DoCs, PVDR ≤ 1.1), and peak and valley doses in a water phantom from a simulated 150 MeV VHEE source were evaluated. Collimator thickness, hole width, and septal width were varied to determine an optimal value for each parameter that maximized PVDR and DoC. The optimized collimator (20 mm thick rectangular prism with a 15 mm × 15 mm face with a 7 × 7 array of 0.5 mm holes separated by 1.1 mm septa) was 3D-printed and used for VHEE irradiations with the CERN linear electron accelerator for research beam. Open beam and mini-GRID irradiations were performed at 140, 175, and 200 MeV and dose was recorded with radiochromic films in a water tank. PVDR, central-axis (CAX) and valley dose rates and DoCs were evaluated. Main results. Films demonstrated peak and valley dose rates on the order of 100 s of MGy/s, which could promote FLASH-sparing effects. Across the three energies, PVDRs of 2–4 at 13 mm depth and DoCs between 39 and 47 mm were achieved. Open beam and mini-GRID MC simulations were run to replicate the film results at 200 MeV. For the mini-GRID irradiations, the film CAX dose was on average 15% higher, the film valley dose was 28% higher, and the film PVDR was 15% lower than calculated by MC. Significance. Ultimately, the PVDRs and DoCs were determined to be too low for a significant potential for SFRT tissue-sparing effects to be present, particularly at depth. Further beam delivery optimization and investigations of new means of spatial fractionation are warranted.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".