Considerations and limitations of fast Monte Carlo electron transport in radiation therapy based on precalculated data
Bibliographic record
Abstract
The purpose of this work is to revisit the impediments and characteristics of fast Monte Carlo techniques for applications in radiation therapy treatment planning using new methods of utilizing pregenerated electron tracks. The limitations of various techniques for the improvement of speed and accuracy of electron transport have been evaluated. A method is proposed that takes advantage of large available memory in current computer hardware for extensive generation of precalculated data. Primary tracks of electrons are generated in the middle of homogeneous materials (water, air, bone, lung) and with energies between 0.2 and 18 MeV using the EGSnrc code. Secondary electrons are not transported, but their position, energy, charge, and direction are saved and used as a primary particle. Based on medium type and incident electron energy, a track is selected from the precalculated set. The performance of the method is tested in various homogeneous and heterogeneous configurations and the results were generally within 2% compared to EGSnrc but with a 40-60 times speed improvement. In a second stage the authors studied the obstacles for further increased speed-ups in voxel geometries by including ray-tracing and particle fluence information in the pregenerated track information. The latter method leads to speed increases of about a factor of 500 over EGSnrc for voxel-based geometries. In both approaches, no physical calculation is carried out during the runtime phase after the pregenerated data has been stored even in the presence of heterogeneities. The precalculated data are generated for each particular material and this improves the performance of the precalculated Monte Carlo code both in terms of accuracy and speed. Precalculated Monte Carlo codes are accurate, fast, and physics independent and therefore applicable to different radiation types including heavy-charged particles.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".