Real-Time Temperature Correction of Medical Range Fiber Bragg Gratings Dosimeters
Bibliographic record
Abstract
The interest in fiber Bragg gratings dosimeters for radiotherapy dosimetry lies in their (i) submillimeter size, (ii) multi-points dose measurements, and (iii) customizable spatial resolution. However, since the radiation measurement relies on the thermal expansion of the surrounding polymer coating, such sensors are strongly temperature dependent, which needs to be accounted for; otherwise, the errors on measurements can be higher than the measurements themselves. In this paper, we test and compare four techniques for temperature compensation: two types of dual grating techniques using different coatings, a pre-irradiation and post-irradiation temperature drift technique, which is used for calorimetry, and finally, we developed a real-time interpolated temperature gradient for the multi-points dosimetry technique. We show that, over these four tested techniques, the last one outperforms the others and allows for real-time temperature correction when an array of 13 fiber Bragg gratings spatially extending over the irradiation zone is used. For a 20 Gy irradiation, this technique reduces the measurement errors from 200% to about 10%, making it suitable for a radiotherapy dose range. Temperature correction for medical low-dose range dosimetry is a first in our field and is essential for clinical FBG dosimetry applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".