Comparison of dose calculation algorithms with Monte Carlo methods for photon arcs
Bibliographic record
Abstract
The objective of this study is to seek an accurate and efficient method to calculate the dose distribution of a photon arc. The algorithms tested include Monte Carlo, pencil beam kernel (PK), and collapsed cone convolution (CCC). For the Monte Carlo dose calculation, EGS4/DOSXYZ was used. The SRCXYZ source code associated with the DOSXYZ was modified so that the gantry angle of a photon beam would be sampled uniformly within the arc range about an isocenter to simulate a photon arc. Specifically, photon beams (6/18 MV, 4 x 4 and 10 x 10 cm2) described by a phase space file generated by BEAM (MCPHS), or by two point sources with different photon energy spectra (MCDIV) were used. These methods were used to calculate three-dimensional (3-D) distributions in a PMMA phantom, a cylindrical water phantom, and a phantom with lung inhomogeneity. A commercial treatment planning system was also used to calculate dose distributions in these phantoms using equivalent tissue air ratio (ETAR), PK and CCC algorithms for inhomogeneity corrections. Dose distributions for a photon arc in these phantoms were measured using a RK ion chamber and radiographic films. For homogeneous phantoms, the measured results agreed well (approximately 2% error) with predictions by the Monte Carlo simulations (MCPHS and MCDIV) and the treatment planning system for the 180 degrees and 360 degrees photon arcs. For the dose distribution in the phantom with lung inhomogeneity with a 90 degrees photon arc, the Monte Carlo calculations agreed with the measurements within 2%, while the treatment planning system using ETAR, PK and CCC underestimated or overestimated the dose inside the lung inhomogeneity from 6% to 12%.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".