Time‐resolved diode dosimetry calibration through Monte Carlo modeling for <i>in vivo</i> passive scattered proton therapy range verification
Bibliographic record
Abstract
PURPOSE: Our group previously introduced an in vivo proton range verification methodology in which a silicon diode array system is used to correlate the dose rate profile per range modulation wheel cycle of the detector signal to the water-equivalent path length (WEPL) for passively scattered proton beam delivery. The implementation of this system requires a set of calibration data to establish a beam-specific response to WEPL fit for the selected 'scout' beam (a 1 cm overshoot of the predicted detector depth with a dose of 4 cGy) in water-equivalent plastic. This necessitates a separate set of measurements for every 'scout' beam that may be appropriate to the clinical case. The current study demonstrates the use of Monte Carlo simulations for calibration of the time-resolved diode dosimetry technique. METHODS: Measurements for three 'scout' beams were compared against simulated detector response with Monte Carlo methods using the Tool for Particle Simulation (TOPAS). The 'scout' beams were then applied in the simulation environment to simulated water-equivalent plastic, a CT of water-equivalent plastic, and a patient CT data set to assess uncertainty. RESULTS: Simulated detector response in water-equivalent plastic was validated against measurements for 'scout' spread out Bragg peaks of range 10 cm, 15 cm, and 21 cm (168 MeV, 177 MeV, and 210 MeV) to within 3.4 mm for all beams, and to within 1 mm in the region where the detector is expected to lie. CONCLUSION: Feasibility has been shown for performing the calibration of the detector response for three 'scout' beams through simulation for the time-resolved diode dosimetry technique in passive scattered proton delivery.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".