SU‐D‐BRB‐06: G4DBR: A Fast Geant4‐Based Monte Carlo Dosimetry Platform for Brachytherapy
Bibliographic record
Abstract
Purpose: To present, G4DBR, a fast Geant4‐based Monte Carlo (MC) dosimetry platform for brachytherapy. The special case of low dose rate (LDR) brachytherapy is considered here. Methods: Geant4 9.3 has been used for designing a new MC platform for calculating the dose distribution in brachytherapy called G4DBR. This code is capable of dealing with the DICOM RT format to build a virtual representation of each patient with the full multi‐seed configuration. The dose is scored both to medium and to water using track‐length estimator. The dose distributions are extracted in 3ddose format for visualization or to calculate the DVHs. Results: One prostate permanent 125I seed (PPSI) and one breast permanent 103Pd seed implant (BSPI) patient have been selected for evaluating the performance of G4DBR on a 2.93 GHz Intel Xeon Nehalem single core. Post‐implant dosimetry of those cases are performed in a 2 mm3 mesh for comparison with BrachyDose and MCPI. 45 seconds were required for G4DBR to reach a statistical uncertainty of 2% on PTV dose in PPSI. Note that for a similar precision, BrachyDose requires 30 seconds on 3 GHz Woodcrest (Thomson et al. Med. Phys. 2010) while MCPI needs 59 seconds on a single 2.4 GHz Pentium 4 CPU (Chibani et al. Med. Phys. 2005). G4DBR takes 114 seconds to attain a statistical uncertainty of less than 2% in the BPSI case. Conclusions: G4DBR is accurate and fast enough for clinical purposes. G4DBR is able to achieve good calculation speeds comparable with BrachyDose and MCPI. Indeed, a statistical uncertainty of less than 2% is attained in 45 seconds in a prostate case while 118 seconds were needed in BPSI to achieve 0.5% of uncertainty. Further developments will include the incorporation of high dose rate (HDR) dosimetry and a user‐friendly GUI for G4DBR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".