Indirect flat‐panel detector with avalanche gain: Fundamental feasibility investigation for SHARP‐AMFPI (scintillator HARP active matrix flat panel imager)
Bibliographic record
Abstract
An indirect flat-panel imager (FPI) with avalanche gain is being investigated for low-dose x-ray imaging. It is made by optically coupling a structured x-ray scintillator CsI(Tl) to an amorphous selenium (a-Se) avalanche photoconductor called HARP (high-gain avalanche rushing photoconductor). The final electronic image is read out using an active matrix array of thin film transistors (TFT). We call the proposed detector SHARP-AMFPI (scintillator HARP active matrix flat panel imager). The advantage of the SHARP-AMFPI is its programmable gain, which can be turned on during low dose fluoroscopy to overcome electronic noise, and turned off during high dose radiography to avoid pixel saturation. The purpose of this paper is to investigate the important design considerations for SHARP-AMFPI such as avalanche gain, which depends on both the thickness d(Se) and the applied electric field E(Se) of the HARP layer. To determine the optimal design parameter and operational conditions for HARP, we measured the E(Se) dependence of both avalanche gain and optical quantum efficiency of an 8 microm HARP layer. The results were used in a physical model of HARP as well as a linear cascaded model of the FPI to determine the following x-ray imaging properties in both the avalanche and nonavalanche modes as a function of E(Se): (1) total gain (which is the product of avalanche gain and optical quantum efficiency); (2) linearity; (3) dynamic range; (4) gain nonuniformity resulting from thickness nonuniformity; and (5) effects of direct x-ray interaction in HARP. Our results showed that a HARP layer thickness of 8 microm can provide adequate avalanche gain and sufficient dynamic range for x-ray imaging applications to permit quantum limited operation over the range of exposures needed for radiography and fluoroscopy.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".