SU-E-T-82: Position-Dependent Discrete Point Spread Functions for EPID IMRT QA
Bibliographic record
Abstract
Purpose: To devise a position-dependent point spread function (PSF) to account for differences in EPID response to X-ray scattering, glare, and photon energy compared to a water slab modeled within a treatment planning system.Method: Since the EPID image corresponds to a discrete pixel grid, the method calculates a discrete, position-dependent, PSF based only on measurements and treatment-planning-system (TPS) results, without resorting to separate Monte Carlo calculations. The PSF is sought in the form of a linear combination of azimuthally-symmetric functions (depending only on the distance between the interaction and scoring point) with position-dependent coefficients (to account for increased off-axis response). The PSF is calculated for a suitable test-pattern, by minimizing the difference, in a least-squares sense, between the acquired EPID image and the image obtained from the superposition of the PSF on the TPS result. Results: The method was tested using numerically-simulated TPS results and EPID measurements. To provide a numerically rigorous test for the method, a 20% increase in off-axis EPID response due to beam softening (higher than the real value) was assumed. An 8 by 6 checkerboard field was used as the test pattern to calculate the PSF which was subsequently applied to a simulated IMRT field consisting of a 20 by 20 array of 5-mm square beamlets with random fluence values. The calculated PSF was able to reproduce the EPID image for the test pattern to within 1% (compared to 15% for a position-invariant PSF) and the EPID image for the IMRT field to within 0.5% (compared to 16% for a position-invariant PSF). Conclusions: A method for calculating a discrete, position-dependent EPID PSF was developed. Preliminary tests show the method to be successful in reproducing simulated X-ray scattering, glare, and off-axis response of the EPID to within 1 %, which makes it promising for IMRT QA.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".