Determination of Tissue Phantom Ratios of High Energy Photon Beams Using Monte Carlo Code
Bibliographic record
Abstract
Monte Carlo codes are veritable tools in radiotherapy to understand the transport mechanism, dose distributions, and energy depositions of ionizing radiations traversing in different media. In some beam calibration protocols, a tissue-phantom-ratio at depths 20 cm and 10 cm (TPR20,10) in a phantom is used to determine the beam-quality conversion factor for different ion chambers. Thus, this study aims to evaluate the efficiency of Monte Carlo code in the determination of beam quality of high radiation energy beams similar to what is expected in clinical settings. Electron Gamma Shower National Research Council in Canada (EGSnrc) Monte Carlo Code was used to design complex ion chamber geometries according to the manufacturer’s specifications. Phase space files were used as x-ray beam radiation sources. Two set-ups (SAD and SSD) methods were used for the determination of TPR20,10 to conform to the accepted clinical procedures. Beam collimation was 10 cm by 10 cm field size with an ionization chamber placed at 20 cm and 10 cm in the water phantom to obtain doses at two points respectively. The TPR20,10 of each energy was obtained using the appropriate equations. The obtained results showed no significant difference between the measured and available TPR 20,10 data (p = 0.995). Ion chamber configurations and specifications were also found not to have a significant effect (p = 0.33) on the TPR 20,10 obtained values. The results obtained in this study are in agreement with the recommended standard values. The findings of this study show that the Monte Carlo codes can be used as a tool in determining x-ray beam quality indices of designed clinical linear accelerator (Linac) machines.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".