Performance evaluation of a Se/CMOS prototype x-ray detector with the Apodized Aperture Pixel (AAP) design
Bibliographic record
Abstract
An x-ray detector’s ability to produce high signal-to-noise ratio (SNR) images for a given exposure is described by the detective quantum efficiency (DQE) as a function of spatial frequency. Current mammography and radiography detectors have poor DQE performance at high frequencies due to noise aliasing when using a high- resolution converter layer. The Apodized-Aperture Pixel (AAP) design is novel detector design that increases high-frequency DQE by removing noise aliasing using smaller sensor elements (eg. 5 - 50 μm) than image pixel size (eg. 50 - 200 μm). The purpose of this work is to implement the AAP design on a selenium (Se) CMOS micro-sensor prototype with 7.8 × 7.8 μm size elements. Conventional (binned) and AAP images with 47 μm pixel size were synthesized and used to measure the modulation transfer function (MTF), normalized Wiener noise power spectrum (NNPS) and DQE. A micro-focus x-ray source (with a tungsten target) and a 60kV beam filtered with 2mm of aluminum was used to measure performance with DQEPro (DQEInstruments Inc., London, Canada) in a dynamic image acquisition mode at a high exposure level (9.7mR). The AAP design has 1.5x greater MTF near the image cut-off frequency (u<sub>c</sub> = 10.6 cyc/mm) than conventional design. DQE near u<sub>c</sub>was 2.5x greater with the AAP design than conventional, and specimen imaging of a kidney stone shows greater SNR of fine-detail in the AAP image.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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 teacher head, 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".