SU-GG-T-460: Liquid Scintillator Dosimetry for Passive Scattering Proton Beam Quality Assurance
Bibliographic record
Abstract
Purpose: A liquid scintillator (LS) detection system has been developed for volumetric characterization of radiation beams. We describe the utility of this system for quality assurance of passively scattered proton beams. Materials and Methods: The detector system consists of a 20×20×20 cm3 volume of LS surrounded by a light tight housing. One side of the tank is transparent to allow viewing by a CCD camera, which is placed 80 cm from the center of the tank. Irradiations were performed at the Proton Therapy Center Houston (PTCH). The tank was irradiated with two spread-out Bragg peaks (SOBPs), one with 13 cm range and 6 cm width, and one with 28.5 cm range and 10 cm width. For the latter, 14 cm of solid water was added before the tank. A median filter was used to correct for radiation noise in the image. The light signal along the beam's central axis was compared to the percent depth dose data measured with an ionization chamber in a water phantom. Results: Light-depth profiles obtained with the LS device are proportional to measured dose in the proximal build-up region of the beam. LET-dependent quenching artifacts decrease the light signal in the last few cm of the proton range, and optical artifacts decrease the signal near the edges of the tank. As a result, the light signal in the SOBP is not proportional to dose. However, the relative light signal can be obtained during quality assurance measurements and compared with baseline data. Conclusion: SOBP light-depth profiles can be obtained by irradiating an LS device with passive proton beams. Despite a non-linear dose response in the SOBP, the device provides sufficient information to measure reliably beam range and SOBP width for daily quality assurance. Supported by the NCI (1R01CA120198-01A2, 2P01CA021239-29A1)
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".