Imaging performance of amorphous selenium based flat-panel detectors for digital mammography: Characterization of a small area prototype detector
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 theoretical model was developed based on cascaded linear system analysis with parallel processes to take 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 small-area prototype detector with 85 microm pixel size. The presampling MTF, NPS, and DQE(f) of the prototype were measured, and compared to the theoretical calculation of the model. The calculation showed that K fluorescence accounted for a 15% reduction in the MTF at the Nyquist frequency (fNy) of the prototype detector, and the NPS at fNy was reduced to 89% of that at zero spatial frequency. The measurement of presampling MTF of the prototype detector revealed an additional source of blurring, which was attributed to charge trapping in the blocking layer at the interface between a-Se and the active matrix. This introduced a drop in both presampling MTF and NPS at high spatial frequency, and reduced aliasing in the NPS. As a result, the DQE(f) of the prototype detector at fNy approached 40% of that at zero spatial frequency. The measured and calculated DQE(f) using the linear system model have reasonable agreement, indicating that the factors controlling image quality in a-Se based mammographic detectors are fully understood, and the model can be used to further optimize detector imaging performance.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".