Penalty on the detective quantum efficiency from off-axis incident x rays
Bibliographic record
Abstract
An often neglected assumption related to detector performance metrics such as the modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE) is that they only apply to a small region around the centre of an x-ray image. In the periphery of an image, image formation is from obliquely incident x rays. These off-axis x rays will introduce an additional degrading effect on the above detector performance metrics. In our study, we use Monte Carlo simulations to quantify the effects of off-axis radiation on the MTF, NPS, and DQE on common diagnostic x-ray detectors. In our simulations, we vary the incident angle of x rays between 0° and 12°, which is a typical range of divergence in diagnostic x-ray imaging. In the case of amorphous selenium, our results show that off-axis incident x rays degrade the MTF above 5 cycles/mm with increasing severity at higher incident angles and x-ray energy, and more importantly has very little effect on the NPS. Hence, the impact is more severe on the DQE due to the MTF squared dependency. For an incident x-ray angle of 12° (~13 cm from central axis or chest wall in mammography), the DQE falls to 50% of its initial value at 10 and 7 cycles/mm for x-ray energies of 20 and 40 keV, respectively. This loss of signal-to-noise ratio may be most significant near the skin line in mammography studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".