Investigation of a direct conversion flat panel imager for portal imaging
Bibliographic record
Abstract
Flat-panel based x-ray imaging is an emerging new technology that could be used to significantly improve the quality of on-line portal imagers. There are two types of flat panel imagers: direct and indirect conversion. Previous experimental work on flat panel detectors for portal imaging application used indirect-conversion imagers. In this paper, a direct-conversion amorphous-selenium flat panel imager is investigated for application in portal imaging. The imager has an active imaging area of 14 in. X 17 in., i.e., 3072 X 2560 pixels each with dimensions 139 microm X 139 microm. The spatial frequency dependent detective quantum efficiency of the imager has been measured for a 6 MV beam and found to be amongst the best area detectors investigated for on-line portal imaging. Effects of changing pixel size, as well as possible improvements to both the image quality and convenience of operation are discussed. Comparison with an indirect conversion flat panel imager is also included.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".