Comprehensive Monte Carlo calculation of the point spread function for a commercial ‐Si EPID
Bibliographic record
Abstract
Images produced by commercial amorphous silicon electronic portal imaging devices (a-Si EPIDs) are subject to multiple blurring processes. Implementation of these devices for fluence measurement requires that the blur be removed from the images. A standard deconvolution operation can be performed to accomplish this assuming the blur kernel is spatially invariant and accurately known. This study determines a comprehensive blur kernel for the Varian aS500 EPID. Monte Carlo techniques are used to derive a dose kernel and an optical kernel, which are then combined to yield an overall blur kernel for both 6 and 15 MV photon beams. Experimental measurement of the line spread function (LSF) is used to verify kernel shape. Kernel performance is gauged by comparing EPID image profiles with in-air dose profiles measured using a diamond detector (approximating fluence) both before and after the EPID images have been deconvolved. Quantitative comparisons are performed using the chi metric, an extension of the well-known y metric, using acceptance criteria of 0.0784 cm (1 pixel width) distance-to-agreement (deltad) and 2% of the relative central axis fluence (deltaD). Without incorporating any free parameters, acceptance was increased from 49.0% of pixels in a cross-plane profile for a 6 MV 10 x 10 cm2 open field to 92.0%. For a 10 x 10 cm2 physically wedged field, acceptance increased from 40.3% to 73.9%. The effect of the optical kernel was found to be negligible for these chi acceptance parameters, however for (deltaD= 1%, deltad = 0.0784 cm) we observed an improvement from 66.1% (without) to 78.6% (with) of chi scores <1 (from 20.6% before deconvolution). It is demonstrated that an empirical kernel having a triple exponential form or a semiempirical kernel based on a simplified model of the detector stack can match the performance of the comprehensive kernel.
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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".