TU-EE-A1-04: Monte Carlo Modelling of the Xoft AXXENT X-Ray Source
Bibliographic record
Abstract
Purpose: To use the EGSnrc Monte Carlo (MC) code for calculations of photon energy spectra and TG-43 dosimetry parameters for Xoft, Inc's miniature x-ray brachytherapy source. The importance of MC treatment planning for brachytherapy is also investigated. Method and Materials: Calculations of in-air photon energy spectra and the dose distribution around the source in water were performed. The radial dose function, anisotropy function, and the absolute dose rate were calculated and compared with measurements made by Rivard et al (submitted to Medical Physics). Calculations were done to investigate how parameters ignored by TG-43 affect dose delivered to the medium. The effects of realistic breast tissue and a finite irradiated volume were investigated. Results: Calculated in air photon spectra show excellent agreement with measurements in the energy range of ∼10–50kV. TG-43 dosimetry parameters agree well with measurements but show a significant dependence on incident electron angles. Comparison of the dose in water to breast tissue show that calculations done in water may overestimate dose to breast tissue. The difference in dose to breast and water varies greatly with distance from the source and differences as large as 18% occur near the source. Calculations done in an infinite medium overestimate dose at the surface by 7% when compared with the case of a source placed 2cm from the surface of a phantom. Conclusion: MC calculations of in-air photon energy spectra and TG-43 dosimetry parameters have been performed and agree well with measurements. Calculations show that by ignoring the effects of realistic tissues and finite irradiated volumes, the TG-43 dosimetry protocol may significantly overestimate dose delivered to a patient. Using MC would improve treatment planning accuracy allowing for better correlation of treatment outcome to dose delivered.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".