SU‐FF‐T‐75: Importance of Contamination Signal Removal On HDR Brachytherapy In Vivo Dosimetry When Using a Scintillating Fiber Dosimeter
Bibliographic record
Abstract
Purpose: To quantify the importance of removing the contamination signal, composed of Cerenkov and fluorescence light, from the output of an in vivo scintillating fiber dosimeter during Iridium‐192 HDR brachytherapy treatments. Method and Materials: The scintillating fiber dosimetry system is composed of a miniature monochrome CCD camera (Apogee Alta U‐4000) detecting light from optical fibers. Two fibers were used for the purpose of this study: one of those had a 3 mm x 1 mm cylindrical scintillator (BCF‐60) coupled to its extremity. Integrating light coming out from both fibers under the same irradiation conditions allows, following proper calibration, to determine the scintillation and the contamination components of the detector signal. This study has been conducted in a solid water phantom. Components of the detector signal have been studied as a function of angular, longitudinal and radial position of the Ir‐192 source with respect to the detecting volume (i.e. scintillator). Results: The contamination component ranged from 4% to 42% of the detector signal, depending on the relative source to scintillator and fiber positions. The highest ratio was obtained when the source was the closest to the scintillator. The lowest was obtained when the source is longitudinally the furthest from the source. The ratio increased from 4% to 10% with the source going from 1cm to 5cm on the radial axis of the scintillator. Angular study reveals that both contamination and scintillation components of the signal varies under 3.4 percents over the complete angles range. Conclusion: Dose determination is proportional to the amount of scintillating light measured. Based on our measurements, the necessity of removing the contamination component of the signal is obvious to obtain an accurate dose calculation. Any scintillating fiber dosimeter for in vivo brachytherapy dosimetry should then include an efficient removal technique of the contamination signal.
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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) | 0.000 | 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".