MO-EE-A1-01: A Necessary and Efficient Cerenkov Subtraction Technique for in Vivo Scintillation Dosimetry for HDR Brachytherapy
Bibliographic record
Abstract
Purpose: To study the Cerenkov contribution in scintillation dosimetry at lower energies using an HDR Ir-192 brachytherapy source and to show the need for its subtraction for accurate in vivo dosimetry. Method and Materials: The detector consisted of a green plastic scintillating fiber coupled to an optical fiber guide. Light was detected using an RGB photodiode connected to a two-channel electrometer (SuperMax model, Standard Imaging), which allows for implementation of different Cerenkov removal techniques. Measurements were performed in solid water. The detector's characteristics were assessed as well as the performance of different Cerenkov filtration techniques. Results from single-color filtration and a more sophisticated chromatic technique were compared to the case where no specific technique was used. A previously benchmarked Monte Carlo study of the dose distribution for the microSelectron V2 Ir-192 radioactive seed was used as the gold standard for comparison. Results: We have shown that this detector is capable of measuring accurately dose rates down to 1 mGy/s and was far from saturation for source distances as small as 2 mm from the scintillator. The Cerenkov component could be easily resolved up to 7 cm from the scintillator. This study demonstrates that when no specific Cerenkov removal technique is used, the detector will yield inaccurate results in certain conditions. Furthermore, the chromatic method clearly outperformed the simple color filter. Finally, the dose rates calculated with the chromatic method were found in good agreement with gold standard data. Conclusion: Including an effective Cerenkov removal technique will enhance the versatility of the plastic scintillation detector and will increase its accuracy. The development of a detector with a built-in Cerenkov removal technique is an important step towards the goal of performing accurate in vivo dosimetry during HDR Ir-192 brachytherapy. Supported by the NCI (1R01CA120198-01A2)
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".