In-Vivo Dosimetry for Ultra-High Dose Rate (UHDR) Electron Beam FLASH Radiotherapy Using an Organic (Plastic), an Organic–Inorganic Hybrid and an Inorganic Point Scintillator System
Bibliographic record
Abstract
Dosimetry is crucial in radiotherapy to warrant safe and correct treatment. In FLASH radiotherapy, where ultra-high dose rates (UHDRs) are used, the dosimetric demands are more stringent, requiring the development and investigation of new dosemeters. In this study, three prototype fiber-optic dosemeters (FODs)—an inorganic, an organic–inorganic hybrid metal halide, and an organic (plastic) scintillator are optimized and investigated for UHDR electron irradiations. The plastic FOD is developed by Medscint, whereas the others are in-house made. The stem signal is minimized by spectral decomposition for the plastic scintillator, and by band-pass wavelength filters for the inorganic and organic–inorganic hybrid metal halide FOD. All prototypes are tested for the dose rate defining parameters. The optimal band-pass wavelength filters are found to be centered around 500 nm and 425 nm for the inorganic and organic–inorganic hybrid metal halide FODs, respectively. A sampling frequency of 1000 Hz is chosen for the inorganic and organic–inorganic hybrid metal halide FODs. The plastic FOD shows to be the least dose rate dependent with maximum deviations of 3% from the reference for the relevant beam settings. The inorganic and organic–inorganic hybrid metal halide FODs, in contrast, show large deviations of >10% from the reference and require more investigation. The current FOD prototypes are insufficient for application in UHDR electron beams, and require further development and investigation.
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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 | 0.000 |
| 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 | 0.000 | 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".