WE‐G‐BRB‐06: Real‐Time Radiation Field Tracking Using Long Scintillating Fibers
Bibliographic record
Abstract
Purpose: To ensure the quality assurance of small field, dynamic radiotherapy, we present and validate a radiation tracking system based on long scintillating fibers that allows for the real‐time measurement of the position and energetic fluence of a small incident radiation field. Method: We aligned 60 parallel scintillating fibers on a thin grooved acrylic slab with a 100‐cm source‐to‐fibers distance. Both ends of each scintillating fiber were coupled to clear optical fibers to enable light collection by a single CCD camera using an f/0.95, 50 mm focal length lens. Using a small, static photon radiation field of 2×2 cm2 of a Varian Clinac iX, we changed the interaction position on the prototype using the linac treatment couch. The interaction position parallel and perpendicular to the scintillating fiber array were deduced using the optical attenuation of the scintillating fibers. The energetic fluence of the incident field was calculated from the fibers light fluxes, corrected for the position dependent optical attenuation and scintillation efficiency. Results: Considering a treatment couch positioning error of ±0.5 mm, the system was able to measure the field position with a mean error of 0.1 mm perpendicular and 0.8 mm parallel to the scintillating fiber array. The maximum error measured using this setup was of 0.13 mm perpendicular and 3.2 mm parallel to the scintillating fiber array. The energetic fluence was determined with a mean error of 0.5% and a maximum error of 2.2%. Conclusions: This work demonstrates the capacity of a long scintillating fibers array to detect in real‐time both the position and the energetic fluence of an incident small radiation field. Such methodology would allow for the real‐time tracking of small field in both photon and particle radiation therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".