Direct in-water radiation dose measurements using Cherenkov emission corrected signals from polarization imaging for a clinical radiotherapy application
Bibliographic record
Abstract
Cherenkov emission (CE) is a visible blueish light emitted in water mediums irradiated by most radiotherapy treatment beams. However, CE is produced anisotropically which currently imposes a geometrical constraint uncertainty for dose measurements. In this work, polarization imaging is proposed and described as a method enabling precise 2D dose measurements using CE. CE produced in a water tank is imaged from four polarization angles using a camera coupled to a rotating polarizer. Using Malus' law, the polarized component of CE is isolated and corrected with Monte Carlo calculated CE polar and azimuthal angular distributions. Projected dose measurements resulting from polarization-corrected CE are compared to equivalent radiochromic film measurements. Overall, agreement between polarized corrected CE signal and films measurements is found to be within 3%, for projected percent depth dose (PPDD) and profiles at the different tested energies ([Formula: see text]: 6 and [Formula: see text], e[Formula: see text]: 6 and 18[Formula: see text]). In comparison, raw Cherenkov emission presented deviations up 60% for electron beam PPDDs and 20% for photon beams PPDDs. Finally, a degree of linear polarization between 29% and 47% was measured for CE in comparison to [Formula: see text]% for scintillation. Hence, polarization imaging is found to be a promising and powerful method for improved radio-luminescent dose measurements with possible extensions to signal separation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".