Sci—Thur PM: YIS — 06: Presentation of a Novel Scintillating Fiber Fluence Monitor for the Real‐Time Verification of Advanced MLC‐Driven Radiotherapy Treatment
Bibliographic record
Abstract
Purpose: To present a novel type of fluence monitoring detector based on optical attenuation for the on‐line quality control of radiotherapy treatments. Method: Long scintillating fibers were aligned along the direction of motion under each of the leaf pair of a Varian Clinac iX MLC and coupled on both ends to clear optical fibers to enable light collection. Following a theoretical model of scintillation collection based on optical attenuation, the detector performance was evaluated by 1) measuring the intrinsic variation of the readings, 2) comparing the experimental data to the expected values calculated from the treatment planning software (TPS) and 3) measuring random leaf errors introduced in an IMRT field. Results: The detector allows the measurement of the central position of the dose deposition on each fiber (xc) and the integral fluence passing through it (Φint). Very low intrinsic dispersion, dominated by Poisson statistics, was observed (under 1mm for xc and under 0.15% for Φint). When compared to the TPS, both xc and Φint) exhibited more significant deviations (respectively a mean of 1.3mm and 2.6%) due to the uncertainties on the dose calculated in regions of high perpendicular dose gradients.Φint is highly sensitive to single leaf motion errors as low as 1mm (at isocenter) while xc showed a good response to a leaf pair translation error of 2mm and more. Conclusion: This work clearly demonstrates the sensitivity and specificity of on‐line quality control of the incident fluence by a thin transmission detector based on optical attenuation of scintillating fibers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".