Extending the IAEA‐AAPM TRS‐483 methodology for radiation therapy machines with field sizes down to 10 × 2 cm<sup>2</sup>
Bibliographic record
Abstract
Purpose The purpose of this study is to provide a calibration methodology for radiation therapy machines where the closest field to the conventional reference field may not meet the lateral charged particle equilibrium (LCPE) condition of the machine‐specific reference (msr) field. We provided two methodologies by extending the International Atomic Energy Agency (IAEA) and the American Association of Physicists in Medicine (AAPM) TRS‐483 code of practice (COP) (Palmans et al. TRS‐483: Dosimetry of small static fields used in external beam radiotherapy: an international code of practice for reference and relative dose determination; 2017) methodology for the calibration of radiation therapy machines with 6 MV flattening filter free (FFF) beam and with field sizes down to 10 × 2 cm2. Methods Two methods of calibration were provided following the TRS‐483. In calibration Method I, the generic correction factors were calculated using Monte Carlo (MC) for seven detectors and rectangular physical field sizes ranging from 10 × 2 cm2 to 10 × 10 cm2. In calibration Method II, we extended the methodology in TRS‐483 for deriving the equivalent square msr field sizes for rectangular field sizes down to 10 × 2 cm2. The beam quality specifier for a hypothetical 10 × 10 cm2 field was derived by extending the methodology provided in the TRS‐483. Since the beam quality correction values for the conventional reference field ( ) tabulated in TRS‐483 are provided only for large reference chambers, we calculated the values analytically for our beam quality specifier and chambers used, using interaction data in TRS‐398 (Andreo, et al. TRS‐398: Absorbed dose determination in external beam radiotherapy: an international code of practice for dosimetry based on standards of absorbed dose to water; 2001). Results The correction values calculated using the first method for chambers with an electrode made of C552 almost did not vary across the different field sizes studied (within 0.1%) while it varied by 1.6% for IBA CC01 with electrode made of steel. Extending the equivalent field and beam quality specifier determination methodology of TRS‐483 resulted in a maximum error of 1.3% on the beam quality specifier for the 2 × 2 cm2 field size. However, this had a negligible impact on the values (less than 0.1%). For chambers with C552 and Al electrode material, the correction factors determined using the two methods of calibration were in agreement to within 0.5%. However, for the chambers with electrode made of higher atomic number (Z), the difference between the two methodologies could be as large as 1.5%. It was shown that this difference can be reduced to less than 0.5% if central electrode perturbation effects and values introduced in TRS‐483 were taken into account. Conclusions In this study, applying the correction values calculated using the calibration Method I to the chamber reading improved the consistency on an absorbed dose determination from 0.5% to 0.1% standard deviation (except for the Exradin A16). For this reason we recommend using calibration Method I. If the values are not available for the user’s detector, calibration Method II can be used to predict the correction factors. However, the second methodology should not be used for chambers with electrode made of high‐Z material unless the electrode perturbation effects and values are taken into account.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".