Improved temporal performance and optical quantum efficiency of avalanche amorphous selenium for low dose medical imaging
Bibliographic record
Abstract
Active matrix flat panel imagers (AMFPIs) with thin-film transistor (TFT) arrays have become the dominant technology for digital x-ray imaging. However, their performance is degraded by electronic noise in low dose imaging applications. One potential solution is to overcome electronic noise using avalanche gain in an amorphous selenium (a-Se) photoconductor in indirect AMFPI, known as the scintillating high-gain avalanche rushing photoconductor AMFPI (SHARP-AMFPI). We previously developed two SHARP-AMFPI prototypes, however both have several areas of desired improvement. In this work, we fabricate and characterize HARP samples with a composite hole blocking layer (HBL) structure to reliably maintain avalanche fields while reducing temporal effects, as well as samples with tellurium (Te) alloyed a-Se to increase the optical quantum efficiency (OQE) to thallium doped cesium iodide (CsI:Tl) columnar scintillators. Our measurements show that the composite HBL has improved temporal performance over the original prototype, with ghosting below 3% at 10 mR equivalent exposure and no noticeable lag observed. We also show that the layer has comparable dark current to the previously used organic HBL and can reach an avalanche gain of 16. We aim to further reduce the dark current by improving the formulation of the n-type metal oxide layer using different deposition methods. Introducing Te-alloying to HARP samples shows an increase in OQE from 0.018 to 0.43 for 532 nm light. The addition of Te resulted in increased lag, attributed to charge trapping within the layer. Future work will investigate arsenic and chlorine co-doping to restore charge transport in this layer.
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 | 0.000 |
| 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".