Model‐based prediction of portal dose images during patient treatment
Bibliographic record
Abstract
PURPOSE: Dosimetric verification of radiation therapy is crucial when delivering complex treatments like intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT. Pretreatment verification, characterized by methods applied without the patient present and before the treatment start date, is typically carried out at most centers. In vivo dosimetric verification, characterized by methods applied with the patient present, is not commonly carried out in the clinic. This work presents a novel, model-based EPID dosimetry method that could be used for routine clinical in vivo patient treatment verification. METHODS: The authors integrated a detailed fluence model with a patient scatter prediction model that uses a superposition of scatter energy fluence kernels, generated via Monte Carlo techniques, to determine patient scatter fluence delivered to the EPID. The total dose to the EPID was calculated using the sum of convolutions of the calculated energy fluence distribution entering the EPID with monoenergetic dose kernels, specific to the a-Si EPID. Measured images with simple, square fields delivered to slab phantoms were validated against predicted images. Measured and predicted images acquired during the delivery of IMRT fields to slabs and an anthropomorphic phantom were compared using the χ-comparison for 3% dose difference and 3 mm distance-to-agreement criteria. RESULTS: Predicted and measured images of the square fields with slabs in the field agreed within 2.5%. Predicted portal dose images of clinical IMRT fields delivered to slabs and an anthropomorphic phantom agreed with measured images within 3% and 3 mm for an average of at least 97% of the infield pixels (defined as >10% maximum field dose) for each case, over all fields. CONCLUSIONS: This work presents the first validation of the integration of a comprehensive fluence model with a patient and EPID radiation transport model that accounts for patient transmission, including complex factors such as patient scatter and the energy response of the a-Si detector. The portal dose image prediction model satisfies the 3% and 3 mm criteria for IMRT fields delivered to slab phantoms and could be used for patient treatment verification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".