Modelling of detective quantum efficiency of direct conversion x-ray imaging detectors incorporating charge carrier trapping and <i>K</i> -fluorescence
Bibliographic record
Abstract
A cascaded linear system model is developed for calculating the frequency-dependent detective quantum efficiency, DQE(f), of a direct conversion x-ray imaging detector by incorporating the effects of charge carrier trapping and reabsorption of K-fluorescent x-rays. The present model considers a combination of series and parallel processes and interactions between them. The modulation transfer function for K-fluorescent x-ray reabsorption is modelled by determining the line spread function and subsequent one-dimensional Fourier transform. The DQE model is applied to amorphous selenium (a-Se) and polycrystalline mercuric iodide (poly-HgI2) detectors. The charge carrier trapping has a significant effect on DQE in both a-Se and poly-HgI2 detectors. The charge carrier transport properties have higher influences on DQE performance in a-Se detectors than that in HgI2 detectors, because of relatively low conversion gain in a-Se detectors. High conversion gain can minimise the adverse effect of incomplete charge collection. A simplified model for the calculation of zero spatial frequency detective quantum efficiency, DQE(0), under parallel cascaded system is also proposed in this study. There exists an optimum photoconductor thickness, which maximises the DQE(0). The proposed model is compared with the published measured data and shows good agreement.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".