Monte Carlo validation of a portal imager scatter dose model
Bibliographic record
Abstract
A common and clinically significant drawback of current portal scatter dose estimation methods is the workload required to measure the scatter dose data for use in the scatter model. To address this problem, a scatter model based on first order Compton scatter was developed and validated for a photon beam energy of 6 MV. This approach was motivated by the observations that at large air gaps (i) the scatter dose is uniform across the portal image (as previously shown by others) and (ii) the scatter dose is principally from first order Compton scatter (shown here). Monte Carlo simulation was chosen for the development and validation of the model since with its use the scatter dose can be separated according to particle type and interaction history, which cannot be done experimentally. The model uses Monte Carlo derived scatter kernels that describe the imager dose from first order scattered photons generated in a 1 cm/sup 3/ voxel located 50 cm or more above the imager. The model includes the divergence and attenuation of the primary and once scattered photons. Dose from multiple scatter was dealt with effectively by slightly overestimating the dose from first scatter. For 36 phantoms (homogeneous, slab, and anthropomorphic), the root mean square deviation between the SPRs calculated using the scatter model and the SPRs from Monte Carlo data was 0.6% or less. For the anthropomorphic phantoms, the authors' model is shown to be comparable in accuracy to a current scatter dose estimation method used for in vivo dosimetry.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".