Calculation of lateral buildup ratio using Monte Carlo simulation for electron radiotherapy
Bibliographic record
Abstract
Monte Carlo simulation was used to calculate the lateral buildup ratio (LBR) used in estimating the percentage depth dose (PDD) and dose per monitor unit for an irregular shaped cutout field in electron radiotherapy. Monte Carlo code BEAMnrc/EGSnrc was used to build a simulation model for a Varian 21 EX linear accelerator producing clinical electron beams with energies of 4, 6, 9, 12, and 16 MeV. The model is optimized by adjusting the incident electron energy within the Monte Carlo simulation so that the calculated PDD curves agree with the measurement within +/-2%. The LBR is calculated from the PDD curves for different diameters of circular cutouts. Although Monte Carlo simulation requires a longer time to create a LBR database compared to measurement using scanning water tank and dosimeter, the simulation models for different electron energies, applicators, and cutouts are very similar. As the calculations can be carried out in a batch mode automatically run by a computer, human efforts in carrying out measurements in the treatment room and fabricating the circular cutouts in the mold room are greatly saved. Moreover, the simulation avoids human error in the experimental setup and can better handle the electron scattering affecting accuracy in the measurement. Using Monte Carlo simulation to calculate the LBR is proved to be useful in the commissioning of the electron beams for electron radiotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".