SU‐C‐224‐05: Application of a Plastic Scintillation Detector in Measuring Depth‐Dose Curves for Passive‐Scattering Proton Beams
Bibliographic record
Abstract
Purpose: The plastic scintillation detector (PSD) has many advantages over other detectors in small field dosimetry due to its high spatial resolution, excellent water equivalency and instantaneous readout. However, in proton beams, the PSDs will undergo a quenching effect which makes the signal level reduced significantly when the detector is close to Bragg peak where the linear energy transfer (LET) for protons is very high. This study investigates the feasibility of using PSDs in depth‐dose measurements for clinical passive‐scattering proton beams. Methods: A polystyrene based PSD (BCF‐12, 0.5mm diameter and 4mm length) was used to measure the depth‐dose curves in a water phantom for pristine proton beams of nominal energies 100, 180, and 250 MeV. A Markus plane‐parallel ion chamber was also used to get the dose distributions for the same proton beams. From these results, the quenching correction factor (QCF) as a function of depth was derived for these proton beams. Next, the LET depth distributions for these proton beams were calculated by using the MCNPX Monte Carlo code, based on the experimentally validated nozzle models for these passive‐scattering proton beams. Then the relationship between the QCF and the proton LET could be derived as an empirical formula. Finally, the obtained empirical formula was applied to the PSD measurements to get the corrected depth‐dose curves and they were compared to the ion chamber measurements. Results: A linear relationship between QCF and LET, i.e. Birkˈs formula, was obtained for the proton beams studied. The PSD measurements after the quenching corrections agree with ion chamber measurements within 5%. Conclusions: PSDs are good dosimeters for proton beam measurement if the quenching effect is corrected appropriately.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".