Investigation of imaging performance of amorphous selenium flat-panel detectors for digital mammography
Bibliographic record
Abstract
Our work is to investigate and understand the factors affecting the imaging performance of amorphous selenium (a-Se) flat-panel detectors for digital mammography. Both theoretical and experimental methods were developed to investigate the spatial frequency dependent detective quantum efficiency [DQE(f)] of a-Se flat-panel detectors for digital mammography. Since the k-edge of a-Se is 12.66 keV and within the energy range of a mammographic spectrum, a cascaded linear system model was developed which takes into account the effect of k-fluorescence on the modulation transfer function (MTF), noise power spectrum (NPS) and DQE(f) of the detector. This model was used to understand the performance of a prototype detector with 85 mm pixel size. The presampling MTF, NPS and DQE(f) of the prototype were measured, and compared to the theoretical calculation by the model. The calculation showed that k-fluorescence reduces the MTF by 15% at the Nyquist frequency (fNY) of the prototype detector, and the NPS at fNY was reduced to 82% of that at zero spatial frequency. Because of the decrease in both MTF and NPS at high spatial frequencies, k-fluorescence only has a small degradation effect on DQE(f) for mammography. The measurement of presampling MTF of the prototype detector revealed an additional source of blurring, which was attributed to the blocking layer at the interface between a-Se and the active matrix. This introduced high frequency drop in both presampling MTF and NPS, and reduced aliasing in the NPS. As a result, the DQE(f) of the prototype detector at fNY approaches 50% of that at zero spatial frequency.
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 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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".