(Invited) Flat-Panel X-ray Image Sensor Using Thin Film Transistors and Field Emitter Arrays
Bibliographic record
Abstract
Active matrix flat panel imagers (AMFPI) have been commercialized for a wide range of x-ray imaging applications. They have demonstrated superior imaging performance except in fluoroscopy, where the electronic noise degrades the image quality, making it inferior to x-ray imaging intensifier (XRII). In this paper we will present two FPI approaches under investigation to overcome the electronic noise limitation. They use an amorphous selenium layer with programmable avalanche gain to detect light generated from an x-ray scintillator upon x-ray absorption. Two charge readout methods are being investigated: a thin-film transistor (TFT) array; and a field emitter array (FEA). The amorphous selenium (a-Se) avalanche photoconductor is called HARP (high-gain avalanche rushing photoconductor). The avalanche gain of HARP depends on both a-Se thickness and applied electric field ESe. At ESe of > 80 V/μm, the avalanche gain can enhance the signal at low dose (e.g. fluoroscopy) and make the detector x-ray quantum noise limited down to a single x-ray photon. At high exposure (e.g. radiography), the avalanche gain can be turned off by decreasing ESe to < 70 V/μm, thus ensuring a wide dynamic range without burdening the readout electronics. The potential x-ray imaging performance of both FPI approaches, especially the aspect of programmable gain to ensure wide dynamic range and x-ray quantum noise limited performance at the lowest exposure in fluoroscopy, have been investigated.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".