Sci-Thurs PM: Delivery-04: Comprehensive fluence model for absolute portal dose image prediction in IMRT pre-treatment verification
Bibliographic record
Abstract
Amorphous silicon (a-Si) electronic portal imaging devices (EPIDs) have been heavily investigated as treatment verification tools, with a particular focus on intensity modulated radiation therapy (IMRT). This verification could be accomplished through a comparison of measured portal images to predicted images. A general fluence determination for portal dose image prediction would be a great asset in order to model the complex modulation of IMRT. A physically-based parameter fluence model was developed by matching multi-leaf collimator defined predicted images to measured image profiles. The fluence model was composed of a focal Gaussian and extrafocal Gaussian-like source (Pearson VII). Specific aspects of the MLCs and secondary collimators were also modeled (eg. jaw and MLC transmission factors, MLC rounded leaf tips, tongue and groove effect, interleaf leakage, MLC offsets). The resulting calculated fluence was then convolved with Monte Carlo generated EPID-specific dose kernels to convert incident fluence to dose delivered to the EPID. Measured EPID data was obtained with an a-Si EPID for various MLC-defined fields (1×1 to 20×20 cm2) over a range of source-to-imager distances. These measured profiles were used to determine the fluence model parameters and the resulting model was tested on prostate and oropharyngeal IMRT fields. The model predicted the open-field profiles within 2%, 2mm, while the predicted IMRT fields were generally within 3%, 3mm for at least 96% of the pixels. This model demonstrates the necessary accuracy needed for IMRT portal dose image prediction in complex clinical examples (<3%, 3mm) and could be used for pre-treatment verification.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".