Application of TOPAS in dose calculations of low energy X-ray therapy machines
Bibliographic record
Abstract
TOPAS (TOol for PArticle Simulation) is a Monte Carlo tool initially designed for simulating proton therapy machines. This study aims to investigate TOPAS’s applicability and accuracy in simulating orthovoltage therapy machine. An Xstrahl 300 orthovoltage machine was simulated. Spectra, HVLs (Half Value Layers), PDDs (Percentage Depth Doses), dose profiles, and backscatter factors were calculated for 100, 180, and 300 kVp. In addition, 3D (Three-Dimensional) dose distributions on a cranial CT (Computed Tomography) were assessed for a 5 cm diameter field size in different treatment locations. Where applicable, comparisons against other measurements or calculations are presented. ”TrackLengthEstimator” method was employed to investigate the dose distributions in regions of interest. Spectra, except for fluorescent peaks, agree with SpekPy (An X-ray spectrum calculator) calculations. HVLs, PDDs, and dose profiles show agreement with clinical measurements. Backscatter factors for four field sizes (1, 3, 5, and 10 cm diameter) and all energies show agreement (difference <2%) with published data. Visualization of dose distributions and DVHs (Dose Volume Histograms) on clinical CT sets are accessible functionalities. TOPAS is a robust Monte Carlo simulation tool for simulations of an orthovoltage treatment unit and could allow accessible visualization of 3D dose distributions in clinical plans. • Orthovoltage unit is characterized using TOPAS simulation tool. • TOPAS is employed to study the dose in a patient. • TOPAS results are verified by measurement or another Monte Carlo method. • This work explores TOPAS capability for in-house orthovoltage treatment planning.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".