Sci-Fri PM: Topics - 01: A monte carlo model of a miniature low-energy x-ray tube using EGSnrc
Bibliographic record
Abstract
The INTRABEAM system (Carl Zeiss, Oberkochen, Germany) is a miniature x-ray generator for use in intraoperative radiotherapy and brachytherapy. The device accelerates electrons to up to 50 keV, which are then steered down an evacuated needle probe to strike a thin gold target. For accurate dosimetry of the INTRABEAM system, it is important that the photon spectrum be well understood. Measurements based on air-kerma are heavily impacted by photon spectra, particularly for low photon energies due to the large photoelectric contribution in air mass energy absorption coefficient. While low energy photons have little clinical significance at treatment depths, they may have a large effect on air-kerma measurements. In this work, we have developed an EGSnrc-based monte carlo (MC) model of the Zeiss INTRABEAM system to study the source photon spectra and half-value layers (HVLs) of the bare probe and with various spherical applicators. HVLs were calculated using the analytical attenuation of air-kerma spectra. The calculated bare probe spectrum was compared with simulated and measured results taken from literature. Differences in the L-line energies of gold were found between the spectra predicted by EGSnrc and Geant4. This is due to M and N shell averaging during atomic transitions in EGSnrc. The calculated HVLs of the bare probe and spherical applicators are consistent with literature reported measured values.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".