An energy fluence‐convolution model for amorphous silicon EPID dose prediction
Bibliographic record
Abstract
In this work, an amorphous silicon electronic portal imaging device (a-Si EPID) dose prediction model based on the energy fluence model of the Pinnacle treatment planning system Version 7 (Philips Medical Systems, Madison, WI) is developed. An energy fluence matrix at very high resolution (< 1 mm) is used to incorporate multileaf collimator (MLC) leaf effects in the predicted EPID images. The primary dose deposited in the EPID is calculated from the energy fluence using experimentally derived radially dependent EPID interaction coefficients. Separate coefficients are used for the open beam energy fluence component and the component of the energy fluence transmitted through closed MLC leaves to each EPID pixel. A spatially invariant EPID dose deposition kernel that describes both radiative dose deposition, central axis EPID backscatter, and optical glare is convolved with the primary dose. The kernel is further optimized to give accurate EPID penumbra prediction and EPID scatter factor with changing MLC field size. An EPID calibration method was developed to reduce the effect of nonuniform backscatter from the support arm (E-arm) in a calibrated EPID image. This method removes the backscatter component from the pixel sensitivity (flood field) correction matrix retaining only field-specific backscatter in the images. The model was compared to EPID images for jaw and MLC defined open fields and eight head and neck intensity modulated radiotherapy (IMRT) fields. For the head and neck IMRT fields with 2%, 2 mm criteria 97.6 +/- 0.6% (mean +/- 1 standard deviation) of points passed with a gamma index less than 1, and for 3%, 3 mm 99.4 +/- 0.4% of points were within the criteria. For these fields, the 2%, 2 mm pass score reduced to 96.0 +/- 1.5% when backscatter was present in the pixel sensitivity correction matrix. The model incorporates the effect of MLC leaf transmission, EPID response to open and MLC leakage dose components, and accurately predicts EPID images of IMRT fields. Removing the backscatter component of the pixel sensitivity matrix correction reduces the effect of nonuniform E-arm backscatter.
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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) | 0.000 | 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".