Physical characterization and performance comparison of active- and passive-pixel CMOS detectors for mammography
Bibliographic record
Abstract
We investigated the physical characteristics of two complementary metal oxide semiconductor (CMOS) mammography detectors. The detectors featured 14-bit image acquisition, 50 microm detector element (del) size and an active area of 5 cm x 5 cm. One detector was a passive-pixel sensor (PPS) with signal amplification performed by an array of amplifiers connected to dels via data lines. The other detector was an active-pixel sensor (APS) with signal amplification performed at each del. Passive-pixel designs have higher read noise due to data line capacitance, and the APS represents an attempt to improve the noise performance of this technology. We evaluated the detectors' resolution by measuring the modulation transfer function (MTF) using a tilted edge. We measured the noise power spectra (NPS) and detective quantum efficiencies (DQE) using mammographic beam conditions specified by the IEC 62220-1-2 standard. Our measurements showed the APS to have much higher gain, slightly higher MTF, and higher NPS. The MTF of both sensors approached 10% near the Nyquist limit. DQE values near dc frequency were in the range of 55-67%, with the APS sensor DQE lower than the PPS DQE for all frequencies. Our results show that lower read noise specifications in this case do not translate into gains in the imaging performance of the sensor. We postulate that the lower fill factor of the APS is a possible cause for this result.
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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 | 0.003 |
| 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 | 0.000 |
| 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".