Sci‐Thur PM – Brachytherapy 01: Fast brachytherapy dose calculations: Characterization of egs_brachy features to enhance simulation efficiency
Bibliographic record
Abstract
Purpose: egs_brachy is a fast, new EGSnrc user‐code for brachytherapy applications. This study characterizes egs_brachy features that enhance simulation efficiency. Methods: Calculations are performed to characterize efficiency gains from various features. Simulations include radionuclide and miniature x‐ray tube sources in water phantoms and idealized prostate, breast, and eye plaque treatments. Features characterized include voxel indexing of sources to reduce boundary checks during radiation transport, scoring collision kerma via tracklength estimator, recycling photons emitted from sources, and using phase space data to initiate simulations. Bremsstrahlung cross section enhancement (BCSE), uniform bremsstrahlung splitting (UBS), and Russian Roulette (RR) are considered for electronic brachytherapy. Results: Efficiency is enhanced by a factor of up to 300 using tracklength versus interaction scoring of collision kerma and by up to 2.7 and 2.6 using phase space sources and particle recycling respectively compared to simulations in which particles are initiated within sources. On a single 2.5 GHz Intel Xeon E5‐2680 processor cor, simulations approximating prostate and breast permanent implant ((2 mm)3 voxels) and eye plaque ((1 mm)3) treatments take as little as 9 s (prostate, eye) and up to 31 s (breast) to achieve 2% statistical uncertainty on doses within the PTV. For electronic brachytherapy, BCSE, UBS, and RR enhance efficiency by a factor >2000 compared to a factor of >104 using a phase space source. Conclusion: egs_brachy features provide substantial efficiency gains, resulting in calculation times sufficiently fast for full Monte Carlo simulations for routine brachytherapy treatment planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".