Evaluation of scintillation detectors for ultrahigh dose-rate x-ray beam dosimetry
Bibliographic record
Abstract
FLASH-Radiotherapy (FLASH-RT) is an emerging radiotherapy technique delivering ionizing radiation beam at ultrahigh dose rates (UHDR), typically ≥40 Gy/s. Animal studies have demonstrated the safety and efficacy of the technique in killing tumor cells while significantly reducing radiation toxicity in normal tissues, compared to conventional radiotherapy (dose-rate exposure <0.03 Gy/s). A reliable real-time dosimeter system is crucial for the characterization of the so-called ‘FLASH-effect’ and an accurate beam delivery. Standard dosimeters for conventional radiotherapy saturate at this high-intensity field or cannot provide real-time measurements. In previous work, optical fiber inorganic scintillating detectors (ISDs) showed excellent linearity with shutter exposure time and tube current, indicating scintillating signals independent of the dose and dose rate, respectively. This study aims to benchmark the performance of the ISD with plastic scintillating detectors (PSDs) for an ultrahigh dose-rate x-ray beam irradiation. Relative scintillator output, signal linearity with dose and dose rate, signal-to-noise ratio (SNR), signal stability and reliability were evaluated for all detectors. In a UHDR x-ray beam irradiation, the ISDs produced a larger SNR than the PSDs. All detectors showed good linearity with tube current (R2 < 0.975) and shutter exposure (R2 >0.999). Gd2O2S:Tb showed excellent repeatability (coefficient of variation (CV) <0.1%) compared to other detectors, while the PSDs resulted in the highest reliability for a UHDR beam measurement with a CV of <0.1%. A further investigation regarding the positioning uncertainty of the ISDs during irradiation due to the detector’s angular dependency and the optimal design of the scintillator detectors for UHDR applications are required.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".