Commissioning of Brachytherapy TPS Using a 2D-Array of Ion Chambers
Bibliographic record
Abstract
Purpose/Objective(s): Unlike external beam treatment planning system (TPS) commissioning, a brachytherapy TPS requires little input, no modeling with most brachytherapy TPS calculations are based on the AAPM TG-43 formalism. The dose distribution using the AAPM TG-43 dose formalism is usually compared with the calculations using the Sievert summation, Monte Carlo simulation or dose distributions measured by with GAFCHROMIC film. These methods have shown an agreement of within 5% compared to the AAPM TG-43 dose formalism. The purpose of this study is to report our experience of using a 2D-array of ion chambers (MatriXX Evolution, IBA Dosimetry) for dosimetric verification of conformal CT-based high dose rate (HDR) brachytherapy.Material/Methods: After benchmarking the new TPS against the old TPS used in the clinical for several years and comparing with MATLAB calculated dose distribution, the dose calculation accuracy of TPS systems was investigated by measuring dose distributions experimentally using MatriXX Evolution. The phantom used for this technique consists of multiple catheters, the IBA MatriXX detector and a slab of RW3 to provide full scattering conditions. The TPS dose distribution was calculated on the CT scan of this phantom. The measured and TPS calculated distributions were compared in IBA Dosimetry OmniPro-I'mRT software. Results: The average absolute dose difference over the ROI was 1.67% and the gamma agreement index computed for a distance to agreement of 3 mm and a dose difference of 3% showed agreement for 98.7% of all pixels, with gamma ≤ 1. Conclusion: We have found that MatriXX 2D dosimetric technique provides a fast and accurate way to validate a brachytherapy TPS for both commissioning and quality assurance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".