Poster — Wed Eve—36: Preliminary Results of Patient Scatter Model for EPID Dosimetry
Bibliographic record
Abstract
Complex radiotherapy techniques, such as intensity modulated radiation therapy (IMRT) or volumetric modulated radiation therapy (VMAT), have greatly increased the motivation for dosimetric verification of radiation therapy treatments. Pretreatment dosimetry is typically carried out prior to a patient's treatment but verification of the actual delivered treatment is not usually performed. Amorphous silicon electronic portal imaging devices (a‐Si EPIDs) have been established for IMRT verification, with one method involving the comparison of a predicted image to a measured image to determine whether the treatment field was delivered correctly. In this work, a comprehensive physics‐based parameter fluence model is interfaced with a patient scatter model to predict portal dose images. The patient scatter algorithm consists of a library of Monte Carlo calculated, scattered photon fluence kernels which predict scattered energy fluence exiting the patient or phantom. Images were acquired with an a‐Si EPID using a 6 MV beam and slab material in the beam path to test the model. Field size ranged from 1×1 to , with solid water thicknesses extending from 1 cm to 25 cm and an air gap of 40 cm. A prostate IMRT field was acquired during a patient treatment for prediction as well. Smaller fields were found to be accurately predicted within 2% and 2 mm, while larger fields were over‐predicted. The prostate field agreed within 2%, 3 mm. The model is able to accurately predict most MLC‐defined fields to within 2% and 2 mm; a prostate IMRT field has also been accurately predicted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".