A small-signal approach to temporal modulation transfer functions with exposure-rate dependence and its application to fluoroscopic detective quantum efficiency
Bibliographic record
Abstract
The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient "dose efficiency." It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. Calculation of this correction factor relies on measurements of the temporal modulation transfer function (MTF). However, the temporal MTF is often exposure-rate dependent, violating a necessary Fourier linearity requirement. The authors show that a Fourier analysis is appropriate for fluoroscopic systems if a "small-signal" approach is used. Using a semitransparent edge, a lag-corrected DQE is described and measured for an x-ray image intensifier-based fluoroscopic system under continuous (non-pulsed) exposure conditions. It was found that results were equivalent for both rising and falling-edge profiles independent of edge attenuation when effective attenuation was in the range of 0.1-0.6. This suggests that this range is appropriate for measuring the small-signal temporal MTF. In general, lag was greatest at low exposure rates. It was also found that results obtained using a falling-edge profile with a radiopaque edge were equivalent to the small-signal results for the test system. If this result is found to be true generally, it removes the need for the small-signal approach. Lag-corrected DQE values were validated by comparison with radiographic DQE values obtained using very long exposures under the same conditions. Lag was observed to inflate DQE measurements by up to 50% when ignored.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".