WE‐E‐BRA‐03: Modelling Ionisation Chamber Response to Non‐Standard Beam Configurations
Bibliographic record
Abstract
Purpose: To present a framework for the calculation of ionisation chamber response to arbitrary modulated fields as a convolution of responses to narrow fields and to test this approach for realistic chamber geometries in open fields. Materials and Methods: Using the EGSnrc/C++ Monte Carlo (MC) class library system, the response of a detailed model of the Exradin #a12 chamber was calculated for and 6 MV pencil beams sweeping over a flat phantom. A pencil beam response kernel was collected for an irregularly spaced array of pencil positions. A procedure was developed to reconstitute chamber response in modulated fields by convolution of pencil beam response over the modulated field fluence. We tested the accuracy of this approach by comparing in open fields the relative calibration coefficients obtained for realistic chamber geometries reconstituted from pencil beam kernels with calibration coefficients obtained directly. Results: MC kQ values within 0.1% and 0.4% of TG‐51 for 6 MV and 18 MV photon beams, respectively. Pencil beam kernels showed that the response of the chamber is strongly dependent on the geometrical details of the chamber for pencil beam positions hitting areas such as chamber tip, tip of the electrode, etc. Open field reconstituted chamber response was in agreement with direct calculations of the open field response to within 0.3% for and 6 MV photon beams. Conclusions: Ionisation chamber response in modulated fields is strongly dependent on the details of the delivery. However, we can accurately account for it with our method, in which chamber response in arbitrarily modulated radiation beams can be calculated by convolving pre‐calculated response kernels over the actual fluence profile used in the delivery of modulated radiotherapy. These techniques will allow guidance of chamber‐based IMRT QA procedures by correcting chamber readings during deliveries that potentially provoke electronic disequilibrium.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".