Performance optimization of a tri‐hybrid method for estimation of patient scatter into the EPID
Bibliographic record
Abstract
Abstract On‐treatment EPID images are contaminated with patient‐generated scattered photons. If this component can be accurately estimated, its effect can be removed, and therefore a corresponding in vivo patient dose estimate will be more accurate. Our group previously developed a "tri‐hybrid" (TH) algorithm to provide fast but accurate estimates of patient‐generated photon scatter. The algorithm uses an analytical method to solve for singly‐scattered photon fluence, a modified Monte Carlo hybrid method to solve for multiply‐scattered photon fluence, and a pencil beam scatter kernel method to solve for electron interaction generated scattered photon fluence. However, for efficient clinical implementation, spatial and energy sampling must be optimized for speed while maintaining overall accuracy. In this work, the most significant sampling issues were examined, including spatial sampling settings for the patient voxel size, the number of Monte Carlo histories used in the modified hybrid MC method, scatter order sampling for the hybrid method, and also a range of energy spectrum sampling (i.e., energy bin sizes). The total predicted patient‐scattered photon fluence entering the EPID was compared with full MC simulation (EGSnrc) for validation. Three phantoms were tested with 6 and 18 MV beam energies, field sizes of 4 × 4, 10 × 10, and 20 × 20 cm 2 , and source‐to‐imager distance of 140 cm to develop a set of optimal sampling settings. With the recommended sampling, accuracy and precision of the total‐scattered energy fluence of the TH patient scatter prediction method are within 0.9% and 1.2%, respectively, for all test cases compared with full MC simulation results. For the mean energy spectrum across the imaging plane, comparison of TH with full MC simulation showed 95% overlap. This study has optimized sampling settings so that they have minimal impact on patient scatter prediction accuracy while maintaining maximum execution speed, a critical step for future clinical implementation.
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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 | 0.001 | 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) | 0.000 | 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".