Poster — Thur Eve — 54: Discrete Point Spread Functions for Electronic Portal Imaging Devices
Bibliographic record
Abstract
EPID images are related to the fluence by a (possibly position‐dependent) Point Spread Function (PSF), or kernel. So far, work concerning finding the PSF for a certain EPID has relied on continuous representations. Since the EPID image itself corresponds to a discrete pixel grid, an interesting problem is to find a discrete PSF and do so based only on measurements, without resorting to Monte Carlo simulations. The present work concerns a method for finding such a discrete kernel for the simple case when the kernel is translation‐ and rotation‐invariant. The method relies on minimizing the difference, in a least‐squares sense, between the true acquired image and the image obtained with the calculated kernel. The method was tested using a simulated fluence and EPID image corresponding to a uniform rectangular field. The EPID image was generated by convolving the fluence with a known PSF. The developed algorithm was then tested by comparing the PSF shape produced by the algorithm (based on the image and fluence) with the actual PSF shape used to construct the EPID image. The two were found to match perfectly. A second test was performed by introducing a random error, uniformly distributed in the (−10%, +10%) interval, into the fluence used as input for the algorithm and then reconstructing the EPID image using the calculated PSF. The difference between the “true” EPID image and the reconstructed one was found to be smaller than 0.1% for the simulated field that was studied.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".