Photon scatter in portal images: Accuracy of a fluence based pencil beam superposition algorithm
Bibliographic record
Abstract
The accuracy of a pencil beam algorithm to predict scattered photon fluence into portal imaging systems was studied. A data base of pencil beam kernels describing scattered photon fluence behind homogeneous water slabs (1-50 cm thick) at various air gap distances (0-100 cm) was generated using the EGS Monte Carlo code. Scatter kernels were partitioned according to particle history: singly-scattered, multiply-scattered, and bremsstrahlung and positron annihilation photons. Mean energy and mean angle with respect to the incident photon pencil beam were also scored. This data allows fluence, mean energy, and mean angular data for each history type to be predicted using the pencil beam algorithm. Pencil beam algorithm predictions for 6 and 24 MV incident photon beams were compared against full Monte Carlo simulations for several inhomogeneous phantoms, including approximations to a lateral neck, and a mediastinum treatment. The accuracy of predicted scattered photon fluence, mean energy, and mean angle was investigated as a function of air gap, field size, photon history, incident beam resolution, and phantom geometry. Maximum errors in mean energies were 0.65 and 0.25 MeV for the higher and lower energy spectra, respectively, and 15 degrees for mean angles. The ability of the pencil beam algorithm to predict scatter fluence decreases with decreasing air gap, with the largest error for each phantom occurring at the exit surface. The maximum predictive error was found to be 6.9% with respect to the total fluence on the central axis. By maintaining even a small air gap (approximately 10 cm), the error in predicted scatter fluence may be kept under 3% for the phantoms and beam energies studied here. It is concluded that this pencil beam algorithm is sufficiently accurate (using International Commission on Radiation Units and Measurements Report No. 24 guidelines for absorbed dose) over the majority of clinically relevant air gaps, for further investigation in a portal dose prediction algorithm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".