Po‐Poster ‐ 16: Correcting geometric distortion of EPID images
Bibliographic record
Abstract
The use of Camera based Electronic Portal Image Devices (EPIDs) for online patient positioning is limited by their performance characteristics including image quality, mechanic stability and both geometric and intensity distortion. Geometric distortion of the EPID images arises in the imaging chain from lens or mirror distortions or from misalignment of the mirror. Correcting this geometric distortion requires mapping the EPID images into flat Cartesian space. In this work, we describe a simple method to measure this image distortion and to develop the appropriate image‐to‐physical space transforms using the properties of the MLC. An well characterized geometric pattern is created in the image plane by the superposition of two images. The individual images are created having every second MLC leaf pair fully closed while the adjacent pair is fully open. A second image is produced using exactly the same leaf configuration, but with the collimator rotated 90 degrees. The composite image is the sum of the two images and produces a well‐defined geometric pattern. A threshold function is applied to the composite image to produce a binary grid and the centroid of each dark/light square was determined. An affine transformation matrix is then generated to map the image of grid back to the virtual grid (physical space), thus correcting the spatial distortion. The mapping function was tested at various gantry angles and demonstrated to be robust.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".