Determination of average LET of therapeutic proton beams using Al<sub>2</sub>O<sub>3</sub>:C optically stimulated luminescence (OSL) detectors
Bibliographic record
Abstract
In this work we present a methodology and proof of concept to experimentally determine average linear energy transfer (LET) of therapeutic proton beams using the optically stimulated luminescence (OSL) of small Al(2)O(3):C detectors. Our methodology is based on the fact that the shape of the OSL decay curve of Al(2)O(3):C detectors depends on the LET of the radiation field. Thus, one can use the shape of the OSL decay curves to establish an LET calibration curve, which in turn permits measurements of LET. We performed irradiations at the M D Anderson Cancer Center Proton Therapy Center, Houston (PTCH), with passive scattering beams. We determined the average LET of the passive scattering beams using a validated Monte Carlo model of the PTCH passive scattering nozzle and correlated them with the shape of the OSL decay curve to obtain an LET calibration curve. Using this calibration curve and OSL measurements, we determined the averaged LET at various water-equivalent depths for therapeutic spread-out Bragg peaks and compared the results with averaged LETs determined using the Monte Carlo simulations. Agreement between measured and simulated fluence-averaged LET was within 24% for low energy spread-out Bragg peak (SOBP) fields and within 14% for high energy SOBP fields. Agreement between measured and simulated dose-averaged LET was within 12% for low energy SOBP fields and within 47% for high energy SOBP fields. The data presented in this work demonstrated the correlation between the OSL decay curve shapes and the average LET of the radiation fields, providing proof of concept of the feasibility of using OSL from Al(2)O(3):C detectors to measure average LET of therapeutic proton beams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".