Noise analysis of a novel hybrid active‐passive pixel sensor for medical X‐ray imaging
Bibliographic record
Abstract
Abstract Passive pixel sensor (PPS) is one of the most widely used architectures in large area amorphous silicon (a‐Si) flat panel imagers. It consists of a detector and a thin film transistor (TFT) acting as a readout switch. While the PPS is advantageous in terms of providing a simple and small architecture suitable for high‐resolution imaging, it directly exposes the signal to the noise of data line and external readout electronics, causing significant increase in the minimum readable sensor input signal. In this work we present the operation and noise performance of a hybrid 3‐TFT current programmed, current output active pixel sensor (APS) suitable for real‐time X‐ray imaging. The pixel circuit extends the application of a‐Si TFT from conventional switching element to on‐pixel amplifier for enhanced signal‐to‐noise ratio and higher imager dynamic range. The capability of operation in both passive and active modes as well as being able to compensate for inherent instabilities of the TFTs makes the architecture a good candidate for X‐ray imaging modalities with a wide range of incoming X‐ray intensities. Measurement and theoretical calculations reveal a value for input refferd noise below the 1000 electron noise limit for real‐time fluoroscopy. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".