Low dose digital X-ray imaging with avalanche amorphous selenium
Bibliographic record
Abstract
Active Matrix Flat Panel Imagers (AMFPI) based on an array of thin film transistors (TFT) have become the dominant technology for digital x-ray imaging. In low dose applications, the performance of both direct and indirect conversion detectors are limited by the electronic noise associated with the TFT array. New concepts of direct and indirect detectors have been proposed using avalanche amorphous selenium (a-Se), referred to as high gain avalanche rushing photoconductor (HARP). The indirect detector utilizes a planar layer of HARP to detect light from an x-ray scintillator and amplify the photogenerated charge. The direct detector utilizes separate interaction (non-avalanche) and amplification (avalanche) regions within the a-Se to achieve depth-independent signal gain. Both detectors require the development of large area, solid state HARP. We have previously reported the first avalanche gain in a-Se with deposition techniques scalable to large area detectors. The goal of the present work is to demonstrate the feasibility of large area HARP fabrication in an a-Se deposition facility established for commercial large area AMFPI. We also examine the effect of alternative pixel electrode materials on avalanche gain. The results show that avalanche gain > 50 is achievable in the HARP layers developed in large area coaters, which is sufficient to achieve x-ray quantum noise limited performance down to a single x-ray photon per pixel. Both chromium (Cr) and indium tin oxide (ITO) have been successfully tested as pixel electrodes.
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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 | 0.000 |
| 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".