<tt>pGPUMCD</tt>: an efficient GPU-based Monte Carlo code for accurate proton dose calculations
Bibliographic record
Abstract
Abstract In proton therapy, Monte Carlo simulations are desirable to accurately predict the delivered dose. This paper introduces and benchmarks pGPUMCD , a GPU-based Monte Carlo code implementing the physical processes required for proton therapy applications. In pGPUMCD , the proton transport is carried out in a voxelized geometry with a class II condensed history scheme. For this purpose, the equivalent restricted stopping power formalism ( L eq formalism), the Fermi–Eyges scattering theory and the discrete electromagnetic/nuclear interactions were considered. pGPUMCD was compared to Geant4 in a validation study where the physical processes were validated one after the other. Dose differences between pGPUMCD and Geant4 were smaller than 1% in the Bragg peak region and up to 3% in its distal fall-off. Moreover, a voxelwise dose difference below 1% was observed for 99.5% of calculation positions. The pGPUMCD 80% falloff positions matched with those of Geant4 within 0.1%. The pGPUMCD computation times were inversely proportional to the voxel size, with one million protons transported in less than 0.5 s with mm 3 voxels. pGPUMCD , based on the L eq formalism variance reduction technique, is therefore an attractive candidate for integration in a clinical treatment planning system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.028 | 0.009 |
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".