Increased temporal resolution of amorphous selenium detector using preferential charge sensing approach
Bibliographic record
Abstract
Amorphous selenium (a-Se) is a direct conversion photoconductor capable of very high spatial resolution that can enable early detection of small and subtle lesions. A-Se also offers cost effective and reliable coupling to large area readout circuitry. Currently, the highest performance commercial flat panel detectors used for mammography are based on a-Se technology. However, this inherent spatial resolution has not been leveraged for real-time imaging applications, e.g. micro- angiography for imaging fine brain vessels that requires spatial resolution approaching 20 lp/mm, which is achievable with selenium technology. The challenge is that a-Se detectors suffer from memory artifacts such as lag that limits the frame rate of the X-ray imager. The frame rate reduction is attributed primarily to lag, which manifests itself as an increased dark conductivity after an X-ray exposure. Increased lag degrades the temporal response of the detector and makes a-Se photoconductor impractical for real-time imaging. Furthermore, high ionization energy required for electron-hole pair creation with a-Se limits the sensitivity of detector for a given X-ray dose, achieving a quantum noise limited system become a challenge. In this study, we investigate preferential sensing of those charge carriers having a higher mobility, i.e. holes for a-Se, to improve the temporal response of a-Se detectors for real-time imaging. A new preferential charge sensing detector with a field shaping internal grid, called Multi Pixel Proportional Counter, is fabricated and tested under the typical clinical usage conditions similar to that of fluoroscopy. The fabricated detector offers high frame rate and low noise imaging through avalanche gain. Conventional a-Se detectors are also fabricated for comparison purposes. Experimental results show that image lag as low as 1% can be achieved with the new structure with an internal grid while the conventional detector exhibits higher lag around 5%.
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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 | 0.000 |
| 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".