Effects of characteristic x rays on the noise power spectra and detective quantum efficiency of photoconductive x‐ray detectors
Bibliographic record
Abstract
The effects of K fluorescence on the imaging performance of photoconductor-based x-ray imaging systems are investigated. A cascaded linear systems model was developed, where a parallel cascaded process was implemented to take into account the effect of K-fluorescence reabsorption on the modulation transfer function (MTF), noise power spectrum (NPS), and the spatial frequency dependent detective quantum efficiency [DQE(f)] of an imaging system. The investigation was focused on amorphous selenium (a-Se), which is the most highly developed photoconductor material for x-ray imaging. The results were compared to those obtained with Monte Carlo simulation using the same imaging condition and detector parameters, so that the validity of the cascaded linear system model could be confirmed. Our results revealed that K-fluorescence reabsorption in a-Se is responsible for a 18% drop in NPS at high spatial frequencies with an incident x-ray photon energy of E=20 keV (which is just above the K edge of 12.5 keV). When E increases to 60 keV, the effects of K-fluorescence reabsorption on NPS decrease to approximately 12% at high spatial frequencies. Because the high frequency drop is present in both MTF and NPS, the effect of K fluorescence on DQE(f) is minimal, especially for E that is much higher than the K edge. We also applied the cascaded linear system model to a newly developed compound photoconductor, lead iodide (PbI2), and found that at 60 keV there is a high frequency drop in NPS of 19%. The calculated NPS were compared to previously published measurements of PbI2 detectors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".