SU‐E‐T‐683: Improvement of LDR Brachytherapy TG‐43 Dose Calculations with a GPU‐Accelerated Raytracing Algorithm
Bibliographic record
Abstract
Purpose: To compare a fast GPU‐based dose calculation algorithm to Monte Carlo (MC) simulations and TG‐43 results in low dose rate brachytherapy, in terms of accuracy and relative execution speed. Methods: Dose calculations were performed in a voxelized numerical phantom comprising bone, air and gold inserts. The source consisted in a single seed of 125I (SelectSeed, Nucletron, The Netherlands). Dose distributions were obtained from calculations based on the TG‐43 formalism, from MC simulations (GEANT4 v.9.3) and from a GPU‐based version of the TG‐43 formalism capable of handling heterogeneities. This feature was implemented in the GPU algorithm by computing the equivalent water length travelled through each voxel between the emission site and the dose calculation point. Results: Dose profiles were plotted along heterogeneities to visualize the behavior of each method, with MC simulation as the gold standard. Unsurprisingly, the TG‐43 method overestimated the dose behind high‐density/high‐Z regions with errors in excess of 100% in some cases. The modified TG‐43 algorithm implemented on the GPU algorithm was able to better reproduce MC results, with errors in the range −9.3% to 10.5% behind the same regions. Larger differences occurred for backscattering effects, which are only modeled in the MC method, but only on a limited region (1–2 mm). The TG‐43 formalism provides dose distributions almost instantaneously while MC simulations typically required up to four hours for statistically significant results. The modified TG‐43 GPU algorithm was able to reproduce MC results in approximately four seconds. Conclusions: For complex geometries, the GPU‐based TG‐43 algorithm was shown to provide dose distributions that are closer to those obtained with MC simulations while being significantly faster. This improvement can potentially improve the planning in LDR brachytherapy, where inter‐source attenuation and tissue heterogeneities were shown to influence dosimetric parameters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".