Experimental investigation of direct-indirect flat-panel imager using tellurium doped amorphous selenium
Bibliographic record
Abstract
Active matrix flat panel imagers (AMFPIs) are widely used in digital radiography, but direct and indirect conversion technologies each have limitations. Direct conversion detectors suffer from low x-ray quantum efficiency, while indirect conversion detectors experience spatial resolution degradation due to optical photon scatter. A direct-indirect “Hybrid” AMFPI, which combines both technologies, has previously shown potential to address these limitations. This hybrid design includes an amorphous selenium (a-Se) layer in contact with a scintillator, functioning as both an x-ray and optical sensor. This study builds on the first Hybrid AMFPI prototype, aiming to improve its detective quantum efficiency (DQE). Two key enhancements were explored: (1) increasing the a-Se layer thickness and (2) improving optical quantum efficiency (OQE) through tellurium (Te) doping. A 6.5 x 6.5 cm² prototype was fabricated with 700 μm a-Se, a Te-doped a-Se optical sensing layer, and a removable 1000 μm CsI:Tl scintillator. The Hybrid configuration showed a 42% (RQA5) and 91% (RQA9) increase in x-ray sensitivity compared to the direct AMFPI, attributed to an approximate tenfold improvement in OQE due to Te doping. The Hybrid achieved a DQE(0) of 0.90 (RQA5) and 0.75 (RQA9), marking it the highest-performing imager for digital radiography applications at RQA9. Preliminary real-time imaging (i.e., 30 frames-per-second) temporal performance measurements indicated minimal ghosting (below 2%) but up to 12% lag, attributed to electron trapping in the Te-doped layer. Future research will explore co-doping with arsenic to enhance electron transport to allow for real-time imaging.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".