High dynamic range pixel architecture for advanced diagnostic medical x-ray imaging applications
Bibliographic record
Abstract
The most widely used architecture in large-area amorphous silicon (a-Si) flat panel imagers is a passive pixel sensor (PPS), which consists of a detector and a readout switch. While the PPS has the advantage of being compact and amenable toward high-resolution imaging, small PPS output signals are swamped by external column charge amplifier and data line thermal noise, which reduce the minimum readable sensor input signal. In contrast to PPS circuits, on-pixel amplifiers in a-Si technology reduce readout noise to levels that can meet even the stringent requirements for low noise digital x-ray fluoroscopy (<1000 noise electrons). However, larger voltages at the pixel input cause the output of the amplified pixel to become nonlinear thus reducing the dynamic range. We reported a hybrid amplified pixel architecture based on a combination of PPS and amplified pixel designs that, in addition to low noise performance, also resulted in large-signal linearity and consequently higher dynamic range [K. S. Karim et al., Proc. SPIE 5368, 657 (2004)]. The additional benefit in large-signal linearity, however, came at the cost of an additional pixel transistor. We present an amplified pixel design that achieves the goals of low noise performance and large-signal linearity without the need for an additional pixel transistor. Theoretical calculations and simulation results for noise indicate the applicability of the amplified a-Si pixel architecture for high dynamic range, medical x-ray imaging applications that require switching between low exposure, real-time fluoroscopy and high-exposure radiography.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".