A Novel Amorphous Selenium Avalanche Detector Structure for Low Dose Medical X-Ray Imaging
Bibliographic record
Abstract
A novel amorphous selenium (a-Se) avalanche detector structure for low dose direct-conversion flat-panel X-ray detector is proposed. The proposed structure contains blocking layers to reduce carrier injection from metal electrodes and hole trapping layer to separate X-ray absorption layer from avalanche gain region. The feasibility of the structure for avalanche gain with negligible avalanche noise is studied by using the semiconductor module of COMSOL multiphysics together with a cascaded linear system. The model considers carrier injection from electrodes and charge carrier transport through various layers of multilayer a-Se structure in order to analyze the transient and steady-state electric field distribution across the detector. A cascaded linear system model that includes reabsorption of K-fluorescent X-rays, carrier trapping in bulk and trapping layer, and avalanche multiplication of charge carrier is used to calculate the frequency-dependent detective quantum efficiency [DQE(f)] and modulation transfer function (MTF) of the proposed structure. The avalanche gain enhances the signal strength and improves the DQE(f) by overcoming the effect of electronic noise at low X-ray doses. The structure is applied for breast tomosynthesis and observed that the proposed structure offers the required avalanche gain to ensure quantum noise limited operation at reduced exposures.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".