X‐ray imaging using avalanche multiplication in amorphous selenium: Investigation of depth dependent avalanche noise
Bibliographic record
Abstract
The past decade has seen the swift development of the flat-panel detector (FPD), also known as the active matrix flat-panel imager, for digital radiography. This new technology is applicable to other modalities, such as fluoroscopy, which require the acquisition of multiple images, but could benefit from some improvements. In such applications where more than one image is acquired less radiation is available to form each image and amplifier noise becomes a serious problem. Avalanche multiplication in amorphous selenium (a-Se) can provide the necessary amplification prior to read out so as to reduce the effect of electronic noise of the FPD. However, in direct conversion detectors avalanche multiplication can lead to a new source of gain fluctuation noise called depth dependent avalanche noise. A theoretical model was developed to understand depth dependent avalanche noise. Experiments were performed on a direct imaging system implementing avalanche multiplication in a layer of a-Se to validate the theory. For parameters appropriate for a diagnostic imaging FPD for fluoroscopy the detective quantum efficiency (DQE) was found to drop by as much as 50% with increasing electric field, as predicted by the theoretical model. This drop in DQE can be eliminated by separating the collection and avalanche regions. For example by having a region of low electric field where x rays are absorbed and converted into charge that then drifts into a region of high electric field where the x-ray generated charge undergoes avalanche multiplication. This means quantum noise limited direct conversion FPD for low exposure imaging techniques are a possibility.
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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 | 0.001 |
| 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".