Apodized-aperture pixel design to increase high-frequency DQE and reduce noise aliasing in x-ray detectors
Bibliographic record
Abstract
The detective quantum efficiency (DQE) of an x-ray detector, expressed as a function of spatial frequency, describes the ability to produce high-quality images relative to an ideal detector. While the DQE normally decreases substantially with increasing frequency, we describe an approach that can be used to improve the DQE response by increasing the DQE at high spatial frequencies. The approach makes use of an apodized-aperture pixel (AAP) design that requires use of a high-resolution x-ray converter such as selenium coupled to a sensor array with very small physical sensor elements, such as CMOS sensors. While sensors with elements of 10 - 25 μm are too small for most practical applications in medical radiography, we describe how larger image pixels of a practical size can be synthesized to provide a better DQE than simple binning or using physical pixels of the same size. A theoretical cascaded-systems analysis shows the DQE at the image sampling cut-off frequency can be improved by up to a factor of 2.5x. The AAP approach was validated experimentally using a CMOS/CsI-based detector having 0.05-mm sensor elements. Using AAP images with 0.2-mm pixels, the high-frequency DQE value was increased from 0.2 to 0.4 compared to simple 4x4 binning. It is concluded that ultra-high-resolution sensors can be used to optimize the high-frequency performance of x-ray detectors and make substantial improvements in image quality for visualization of small stuctures and fine image detail in comparison to current imaging systems.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".