Sci-Sat AM(2): Brachy - 05: Fast Monte Carlo Dose Calculations for Brachytherapy with BrachyDose
Bibliographic record
Abstract
A fast dose calculation algorithm called BrachyDose has been developed for brachytherapy applications. BrachyDose is based on the EGSnrc code system for simulating radiation transport. Complex geometries are modelled through the superposition of basic geometric entities (spheres, cuboids, cylinders, and cones) using Yegin's multi-geometry package; the phantom geometry may be defined using a CT dataset. A database of brachytherapy sources has been developed and benchmarked, as has a database of eye plaque applicators. BrachyDose scores collision kerma, which is equivalent to absorbed dose for most situations of interest, using a tracklength estimator. The phase space of particles emitted from brachytherapy sources may be generated with BrachyDose and used in subsequent simulations to avoid the repeated simulation of particle transport within sources. A particle recycling feature has been implemented for multisource configurations in which the first source acts as a particle generator; particles emitted from this source are reinitiated at each source location. Dose calculations for prostate permanent implants achieving 2% average uncertainty in the prostate region take less than 30 seconds in (2 mm)3 voxels on a single 3.0 GHz Woodcrest core; calculation times for eye plaque therapy are on the order of three minutes in (0.5 mm)3 voxels. These calculation times are sufficiently fast for routine clinical treatment planning. A graphical user interface (GUI) for BrachyDose has been developed. Working towards clinical implementation, efforts are underway to integrate data in the DICOM-RT format with BrachyDose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.226 | 0.083 |
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".