X‐ray imaging with amorphous selenium: X‐ray to charge conversion gain and avalanche multiplication gain
Bibliographic record
Abstract
Fluoroscopy is a low dose imaging technique. As such, a very sensitive detector is required to create images of good quality. Present day flat panel active matrix read out systems introduce an amount of noise that inhibits present direct and indirect methods from producing optimal quality images at fluoroscopic exposure rates (0.1-10 microR per frame). The gain of the direct conversion approach using amorphous selenium (a-Se) was investigated to determine whether by increasing the applied electric field, a gain sufficient to overcome the noise limitations of the active matrix could be achieved. Conversion gain and avalanche multiplication in a-Se were investigated as a function of electric field from 10 to 100 V/microm. Our results show a factor of 4 increase in conversion gain is available by increasing electric field from the current standard of 10 V/microm to 100 V/microm. Furthermore, we show that avalanche multiplication can provide an additional gain of up to 25. This increase in signal is sufficient to overcome the noise level encountered in flat panel detectors and permit fully quantum noise limited operation across the whole fluoroscopic range of exposure.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".