Proton bunch monitors for the clinical translation of prompt gamma-ray timing
Bibliographic record
Abstract
Abstract Objective. Prompt gamma-ray timing is an emerging technology in the field of particle therapy treatment verification. This system measures the arrival times of gamma rays produced in the patient body and uses the cyclotron radio frequency signal as time reference for the beam micro-bunches. Its translation into clinical practice is currently hindered by observed instabilities in the phase relation between the cyclotron radio frequency and the measured arrival time of prompt gamma rays. To counteract this, two proton bunch monitors are presented, integrated into the prompt gamma-ray timing workflow and evaluated. Approach. The two monitors are (a) a diamond detector placed at the beam energy degrader, and (b) a cyclotron monitor signal measuring the phase difference between dee current and voltage. First, the two proton bunch monitors as well as their mutual correlation were characterized. Then, a prompt gamma-ray timing measurement was performed aiming to quantify the present magnitude of the phase instabilities and to evaluate the ability of the proton bunch monitors to correct for these instabilities. Main results. It was found that the two new monitors showed a very high correlation for intermediate proton energies after the first second of irradiation, and that they were able to reduce fluctuations in the detected phase of prompt gamma rays. Furthermore, the amplitude of the phase instabilities had intrinsically decreased from about 700 ps to below 100 ps due to cyclotron upgrades. Significance. The uncertainty of the prompt gamma-ray timing method for proton treatment verification was reduced. For routine clinical application, challenges remain in accounting for detector load effects, temperature drifts and throughput limitations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".