High dynamic range active pixel sensor arrays for digital x-ray imaging using a-Si:H
Bibliographic record
Abstract
Hydrogenated amorphous silicon (a-Si:H) active matrix flat panel imagers have gained considerable significance in large area digital imaging applications, in view of their large area readout capability. Current interests lie in a multifaceted a-Si:H array which is compatible with multiple x-ray imaging modalities. This concept entails a single detector system with sufficient dynamic range and variable signal gain. This article reports on an active pixel sensor (APS) array with high dynamic range and programable gain for multimodality x-ray imaging. Initial results have demonstrated sensitivity from subpicoampere to nanoampere photocurrent, which proves amenable for both low-dosage dynamic imaging and high input static imaging. In addition, the programable system signal gain alleviates problems such as output saturation and ensures signal readout linearity to further improve the exploitable dynamic range. Together with external amplification, this APS circuit delivers the performance needed in terms of signal gain, dynamic range, and readout rate entailed by fluoroscopic and radiographic imaging applications.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".