Development of a portal dose image prediction algorithm for arbitrary detector systems
Bibliographic record
Abstract
This thesis presents the development of a two‐step model that predicts dose deposition in arbitrary portal image detectors. The algorithm requires patient computed tomographic data, source‐detector distance, and knowledge of the incident photon beam fluence. The first step predicts the photon fluence entering a portal imaging detector located behind the patient. Primary fluence is obtained through simple ray tracing techniques, while scatter fluence prediction requires a library of scatter fluence kernels generated by Monte Carlo simulation. These kernels allow prediction of basic radiation transport parameters characterizing the scattered photons, including fluence and energy. The second step of the algorithm involves a superposition of Monte Carlo‐generated pencil beam kernels, describing dose deposition in a specific detector, with the predicted incident fluence of primary and scattered photons. The algorithm is tested on a variety of simple slab and anthropomorphic phantoms. Clinical parameters were varied over a wide range of interest, including 6, 18, and 23 MV photon beam spectra and 10–80 cm air gap between phantom and portal imaging detector. Both low and high atomic number detectors were used to verify the algorithm, including a linear array of fluid ionization chambers and a solid state, amorphous silicon detector. Agreement between predicted and measured portal dose is better than 5% in areas of low dose gradient (<30%/cm) and better than 5 mm in areas of high dose gradient (>30%/cm) for the variety of situations tested here. It is concluded that this portal dose prediction algorithm is fast, accurate, allows separation of primary and scatter dose, and can model dose image formation in arbitrary detector systems.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".