SU‐E‐T‐476: GPU‐Based Monte Carlo Radiotherapy Dose Calculation Using Phase‐ Space Sources
Bibliographic record
Abstract
PURPOSE: To design an efficient method for utilizing phase-space source models in the GPU-based Monte Carlo (MC) dose calculation engine gDPM. METHODS: In GPU-based MC algorithms, particles are transported in parallel on different threads. Particles of different types and energies can require significantly different execution times. This can cause "thread divergence" and lower efficiency when source particles are read sequentially from a phase-space file. We have developed a strategy for utilizing phase- space files in a GPU compatible manner whereby the particles are grouped into phase-space-lets (PSLs) by type, energy, and location in the phase- space plane. This allows for dose calculations using only particles inside the field opening defined by the secondary collimators. For validation, the gDPM PSL implementation is compared with DOSXYZnrc using a BEAMnrc phase-space source model as input. RESULTS: Two phase-spaces were generated using a BEAMnrc head model of a 6MV Varian Clinac 21EX, one above the upper jaws used to generate PSLs for gDPM and the other below the lower jaws used for DOSXYZnrc dose calculation. Profiles and depth dose curves for a variety of field sizes were generated in a water phantom. The agreement between gDPM and DOSXYZnrc is within 2% for all field sizes. For the 10 cm × 10 cm field, the calculation times of 650 million histories were 147 CPU hours and 54 GPU seconds for DOSXYZnrc and gDPM, respectively. In addition, we have tested the gDPM PSL implementation for dose calculation in a realistic 7-field IMRT tongue treatment plan. The calculation times were 59 CPU-hours and 66 GPU- seconds for DOSXYZnrc and gDPM for 485 million histories, respectively. Gamma pass rate for the two dose distributions was 99.54% for 3 mm/3% criteria within the 10% isodose. CONCLUSIONS: Methods for the efficient use of phase-space sources for GPU-based MC dose calculations have been developed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".