<title>Dependence of the DQE of photoconductive x-ray detectors on charge transport and trapping</title>
Bibliographic record
Abstract
The effects of charge carrier trapping (i.e. incomplete charge collection) on the detective quantum efficiency (DQE) of a photoconductive detector are studied by using a cascaded linear system model. The model includes signal and noise propagations in the following stages: (1) x-ray attenuation, (2) conversion gain, (3) charge collection, (4) the addition of electronic noise. We examine the DQE(0) of a-Se for fluoroscopy application as a function of photoconductor thickness with varying amounts of electronic noise under (a) constant field, and (b) constant voltage operating conditions. We show that there is an optimum photoconductor thickness, which maximizes the DQE(0) under a constant voltage operation. The optimum thickness depends on the added electronic noise, x-ray exposure, bias voltage and polarity. The actual broad x-ray spectrum emitted from a typical x-ray tube is used in the calculation. The DQE for the negative bias is significantly lower than that of the positive bias, and the diversity in DQE, as expected, increases with the photoconductor thickness because of the asymmetric transport properties of holes and electrons in a-Se. The present results show that the DQE generally does not continue to improve with greater photoconductor thickness in the presence of added electronic noise because of charge transport and trapping effects.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".