TU‐C‐AUD B‐01: Using the Air/water Interface to Improve the Accuracy of Entrance Dosimetry
Bibliographic record
Abstract
Purpose: To improve ionization chamber localization accuracy for depth‐dose measurements used for TPS dose calculation algorithm commissioning and periodic linear accelerator QA. Method and Materials: Ionization chamber depth‐dose scans are set to include points above the water surface, which produces inflections in the depth‐dose curves. Monte Carlo simulations are performed with the EGSnrc Cavity usercode, which simulates the detailed ionization chamber and phantom geometries, and with DOSXYZnrc, which excludes the chamber geometry. The inflection point location in the Cavity simulation with respect to the chamber center quantifies the chamber's absolute location. The difference between the Cavity and DOSXYZnrc depth‐dose results quantifies the ion chamber's effective point of measurement (EPOM) variation as a function of depth. Measurements and simulations are performed for 6 and 18 MV photon beams for multiple field sizes. Measurement results are aligned to the surface position by matching the computed inflection points. Results: The dose inflection point due to the air‐water interface is clearly identifiable in both measurements and calculations. A Cavity simulation at 6 MV with a 10×10 cm 2 field finds that the inflection point occurs when the central electrode is ∼1 mm beneath the water surface. After applying the recommended EPOM shift to Cavity simulation results, the distance‐to‐agreement between the Cavity computed “surface” dose and the DOSXYZnrc dose was >2 mm. By 1.0 cm depth, the distance‐to‐agreement is negligible. 18 MV simulations yielded discrepancies in the in‐air dose, presumably due to differences in contaminant electrons. Conclusion: The proposed method of conducting depth‐dose measurements is trivial to implement and provides a way to automatically account for, and correct, shifts and/or offsets in initial chamber positioning. This allows for improved matching, not only of measured and calculated data, but also of measured data such as that acquired in periodic QA testing.
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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".