Investigation of voxel warping and energy mapping approaches for fast 4D Monte Carlo dose calculations in deformed geometries using VMC++
Bibliographic record
Abstract
A new deformable geometry class for the VMC++ Monte Carlo code was implemented based on the voxel warping method. Alternative geometries which use tetrahedral sub-elements were implemented and efficiency improvements investigated. A new energy mapping method, based on calculating the volume overlap between deformed reference dose grid and the target dose grid, was also developed. Dose calculations using both the voxel warping and energy mapping methods were compared in simple phantoms as well as a patient geometry. The new deformed geometry implementation in VMC++ increased calculation times by approximately a factor of 6 compared to standard VMC++ calculations in rectilinear geometries. However, the tetrahedron-based geometries were found to improve computational efficiency, relative to the dodecahedron-based geometry, by a factor of 2. When an exact transformation between the reference and target geometries was provided, the voxel and energy warping methods produced identical results. However, when the transformation is not exact, there were discrepancies in the energy deposited on the target geometry which lead to significant differences in the dose calculated by the two methods. Preliminary investigations indicate that these energy differences may correlate with registration errors; however, further work is needed to determine the usefulness of this metric for quantifying registration accuracy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.000 |
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".