Direct selenium x-ray detector for fluoroscopy, R&F, and radiography
Bibliographic record
Abstract
The use of selenium alloys for direct conversion fluoroscopy flat-panel detectors has been underestimated. The purpose of this paper is to demonstrate the salient features of a selenium-based detector designed for R&F applications. The detector has an active area of 30 cm X 27 cm and comprises 2048 X 1792 pixels at 150 micrometer pitch. The geometric fill factor is 66%, but experimental evidence supports the fact that internal electric field bending leads to an effective fill factor approaching unity. The detector is designed to support full resolution images at 15 frames/second, and 896 X 1024 resolution at 30 frames/second. The detector is coated with a simple coplanar 'p-i-n' selenium diode structure which has a dark current less than 100 pA/cm2. The thickness of this structure is 1000 micrometer to absorb 77% of a NEMA standard fluoroscopy beam. Measurements show we have obtained an x-ray sensitivity of 4400pC/mR/cm2, which translates to 1212 collected charges per absorbed x-ray. Resolution was measured to be near the theoretically predicted values, with a modulation of 63% at the Nyquist limit of 3.33 lp/mm. Phantom images were obtained at a frame rate of 15 frames per second, and negligible lag was observed in this image sequence.
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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) | 0.001 | 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) | 0.010 | 0.003 |
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".