Sci‐Fri AM: YIS‐10: Development of a flat panel detector with avalanche gain for low‐dose x‐ray imaging
Bibliographic record
Abstract
Digital flat panel detectors are increasingly being used in radiography and fluoroscopy. The imaging performance of current systems, however, is compromised by electronic noise at the low X-ray exposures employed in fluoroscopy and low-dose radiography. In other words, current flat panel detectors are not quantum noise limited at these low radiation exposures. There is thus a need to develop an imaging detector with the high sensitivity of an X-ray image intensifier and the inherent advantages of a solid-state flat panel detector. Towards this end, we have developed and characterized a novel solid-state device capable of providing very high avalanche gains and an excellent temporal response. The device which is based on the amorphous photoconductor a-Se, is scalable (i.e. can be manufactured in large areas), can overcome electronic noise even at the lowest X-ray exposures used in diagnostic imaging (0.1 μR/frame at the detector) and has a very low level of dark current. Here, we investigate the gain and temporal characteristics of this device and discuss its applicability for low exposure X-ray imaging as well as the effects of avalanche gain on the detective quantum efficiency. Coupled to a high-resolution structured CsI X-ray scintillator and a thin film transistor array, this device should provide a true solid-state alternative to the X-ray image intensifier, which is both robust and cost-effective. This should open the door to dose-efficient flat panel imagers for radiography and fluoroscopy as well as a number of other demanding medical imaging applications.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".