Amplified pixel sensor architectures for low dose computed tomography using silicon thin film technology
Bibliographic record
Abstract
Cone beam computed tomography (CBCT) has been recently reported using flat panel imagers (FPI). Here, detector technology capable of high speed imaging, high spatial resolution, large volume coverage, better contrast resolution and, in particular, lowered patient dose is required. Employing active matrix flat panel imagers (AMFPIs) as cone beam CT detectors has been proposed as a solution for improving volume coverage, contrast and resolution; however, clinical evaluations have shown that they suffer from low speed read out. Unlike passive pixel architecture which is currently the state-of-the-art technology for AMFPIs, our preliminary studies have shown that novel amplified pixel sensor (APS) architectures can overcome the low readout speed, and moreover, they provide gain which can be traded for higher frame rate and lower X-ray doses. Although APS architectures can meet the high dynamic range and low noise requirements of CT imaging, linearity and variations between pixel characteristics are major issues. In this study we will investigate novel APS architectures to address these concerns.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".