SU‐C‐201‐07: Towards Clinical Cherenkov Emission Dosimetry: Stopping Power‐To‐Cherenkov Power Ratios and Beam Quality Specification of Clinical Electron Beams
Bibliographic record
Abstract
Purpose: We propose a Cherenkov emission (CE)‐based reference dosimetry method, which in contrast to ionization chamber‐based dosimetry, employs spectrum‐averaged electron restricted mass collision stopping power‐to‐Cherenkov power ratios (SCRs), and we examine Monte Carlo‐calculated SCRs and beam quality specification of clinical electron beams. Methods: The EGSnrc user code SPRRZnrc was modified to compute SCRs instead of stopping‐power ratios (single medium: water; cut‐off: CE threshold (observing Spencer‐Attix conditions); CE power: Frank‐Tamm). SCRs are calculated with BEAMnrc for realistic electron beams with nominal energies of 6–22 MeV from three Varian accelerators (TrueBeam Clinac 21EX, Clinac 2100C/D) and for mono‐energetic beams of energies equal to the mean electron energy at the water surface. Sources of deviation between clinical and mono‐energetic SCRs are analyzed quantitatively. A universal fit for the beam‐quality index R50 in terms of the depth of 50% CE C50 is carried out. Results: SCRs at reference depth are overestimated by mono‐energetic values by up to 0.2% for a 6‐MeV beam and underestimated by up to 2.3% for a 22‐MeV beam. The variation is mainly due to the clinical beam spectrum and photon contamination. Beam angular spread has a small effect across all depths and energies. The influence of the electron spectrum becomes increasingly significant at large depths, while at shallow depths and high beam energies photon contamination is predominant (up to 2.0%). The universal data fit reveals a strong linear correlation between R50 and C50 (ρ > 0.99999). Conclusion: CE is inherent to radiotherapy beams and can be detected outside the beam with available optical technologies, which makes it an ideal candidate for out‐of‐beam high‐resolution 3D dosimetry. Successful clinical implementation of CE dosimetry hinges on the development of robust protocols for converting measured CE to radiation dose. Our findings constitute a key step towards clinical CE dosimetry.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| 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".