On the feasibility of using an optical fiber Bragg grating array for multi-point dose measurements in radiation therapy
Bibliographic record
Abstract
Abstract Fiber Bragg gratings (FBGs) have proven to be a valuable dosimeter in nuclear environment where radiation doses reach up to a hundred of kiloGray (kGy). Multiple FBGs can be written in a single fiber to allow multi-point detection which would prove very useful for radiotherapy dosimetry. The purpose here is to adapt this already existing technology to provide a novel dosimeter for radiotherapy measurements. The proposed real-time dosimeter consists of twenty 4 mm-long FBGs, equally distributed over 20 cm. FBGs are written through the coating of a standard polyimide-coated silica fiber with the phase-mask technique and femtosecond pulses. The wavelength dependant variation of each FBG is recorded at 1 kHz with a commercially available interrogator. The use of gamma radiation (clinical radiotherapy accelerator) induces a linear shift (0.070 ± 0.006 pm/Gy) of the FBG’s reflected wavelength, which is independent of the dose rates (2.8-11.6 Gy/min) and the energy (6-23 MV). A statistical error of 0.03 pm is obtained on data points therefore limiting the detectable dose to 0.4 Gy. A dose profile of 6 and 23 MV radiotherapy accelerator is also measured. The presented FBGs dosimeter allows for real-time dose measurement in 2D and the small size of its detector makes it a versatile tool. The length and spacing of FBGs can be easily modified to increase both the spatial resolution and the amount of dose point.
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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 | 0.000 | 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".