Effects of characteristic x rays on the noise power spectra and detective quantum efficiency of photoconductive x‐ray detectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it