Effect of repeated x‐ray exposure on the resolution of amorphous selenium based x‐ray imagers
Bibliographic record
Abstract
PURPOSE: A numerical model and the experimental methods to study the x-ray exposure dependent change in the modulation transfer function (MTF) of amorphous selenium (a-Se) based active matrix flat panel imagers (AMFPIs) are described. The physical mechanisms responsible for the x-ray exposure dependent change in MTF are also investigated. METHODS: A numerical model for describing the x-ray exposure dependent MTF of a-Se based AMFPIs has been developed. The x-ray sensitivity and MTF of an a-Se AMFPI have been measured as a function of exposure. The instantaneous electric field and free and trapped carrier distributions in the photoconductor layer are obtained by numerically solving the Poisson's equation, continuity equations, and trapping rate equations using the backward Euler finite difference method. From the trapped carrier distributions, a method for calculating the MTF due to incomplete charge collection is proposed. RESULTS: The model developed in this work and the experimental data show a reasonably good agreement. The model is able to simultaneously predict the dependence of the sensitivity and MTF on accumulated exposure at different applied fields and bias polarities, with the same charge transport parameters that are typical of the particular a-Se photoconductive layer that is used in these AMFPIs. Under negative bias, the MTF actually improves with the accumulated x-ray exposure while the sensitivity decreases. The MTF enhancement with exposure decreases with increasing applied field. CONCLUSIONS: The most prevalent processes that control the MTF under negative bias are the recombination of drifting holes with previously trapped electrons (electrons remain in deep traps due to their long release times compared with the time scale of the experiments) and the deep trapping of drifting holes and electrons.
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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) | 0.001 | 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".