A GPU implementation of EGSnrc's Monte Carlo photon transport for imaging applications
Bibliographic record
Abstract
EGSnrc is a well-known Monte Carlo simulation package for coupled electron-photon transport that is widely used in medical physics application. This paper proposes a parallel implementation of the photon transport mechanism of EGSnrc for graphics processing units (GPUs) using NVIDIA's Compute Unified Device Architecture (CUDA). The implementation is specifically designed for imaging applications in the diagnostic energy range and does not model electrons. No approximations or simplifications of the original EGSnrc code were made other than using single floating-point precision instead of double precision and a different random number generator. To avoid performance penalties due to the random nature of the Monte Carlo method, the simulation was divided into smaller steps that could easily be performed in a parallel fashion suitable for GPUs. Speedups of 20 to 40 times for 64(3) to 256(3) voxels were observed while the accuracy of the simulation was preserved. A detailed analysis of the differences between the CUDA simulation and the original EGSnrc was conducted. The two simulations were found to produce equivalent results for scattered photons and an overall systematic deviation of less than 0.08% was observed for primary photons.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".