SU-D-304-06: Measurement of LET in Patient-Specific Proton Therapy Treatment Fields Using Optically Stimulated Luminescence Detectors
Bibliographic record
Abstract
Purpose: To investigate the use of optically stimulated luminescence (OSL) detectors (OSLDs) for measurements of dose-averaged linear energy transfer (LET) in patient-specific proton therapy treatment fields. Methods: We used Al2O3:C OSLDs made from the same material as commercially available nanoDot OSLDs from Landauer, Inc. We calibrated two parameters of the OSL signal as functions of LET in therapeutic proton beams: the ratio of the ultraviolet and blue emission intensities (UV/blue ratio) and the OSL curve shape. These calibration curves were created by irradiating OSLDs in passively scattered beams of known LET (0.96 to 3.91 keV/µm). The LET values were determined using a validated Monte Carlo model of the beamline. We then irradiated new OSLDs with the prescription dose (16 to 74 cGy absorbed dose to water) at the center of the spread-out Bragg peak (SOBP) of four patient-specific treatment fields. From readouts of these OSLDs, we determined both the UV/blue ratio and OSL curve shape parameters. Combining these parameters with the calibration curves, we were able to measure LET using the OSLDs. The measurements were compared to the theoretical LET values obtained from Monte Carlo simulations of the patient-specific treatments fields. Results: Using the UV/blue ratio parameter, we were able to measure LET within 3.8%, 6.2%, 5.6% and 8.6% of the Monte Carlo value for each of the patient fields. Similarly, using the OSL curve shape parameter, LET measurements agreed within 0.5%, 11.0%, 2.5% and 7.6% for each of the four fields. Conclusion: We have demonstrated a method to verify LET in patient-specific proton therapy treatment fields using OSLDs. The possibility of enhancing biological effectiveness of proton therapy treatment plans by including LET in the optimization has been previously shown. The LET verification method we have demonstrated will be useful in the quality assurance of such LET optimized treatment plans. DA Granville received financial support from the Natural Sciences and Engineering Research Council of Canada
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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 | 0.000 |
| 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 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".