A moving slanted‐edge method to measure the temporal modulation transfer function of fluoroscopic systems
Bibliographic record
Abstract
Lag in fluoroscopic systems introduces a frame-averaging effect that reduces measurements of image noise and incorrectly inflates measurements of the detective quantum efficiency (DQE). A correction can be implemented based on measurements of the temporal modulation transfer function (MTF). We introduce a method of measuring the temporal MTF under fluoroscopic conditions using a moving slanted edge, a generalization of the slanted-edge method used to measure the (spatial) MTF, providing the temporal MTF of the entire imaging system. The method uses a single x-ray exposure, constant edge velocity, and assumes spatial and temporal blurring are separable. The method was validated on a laboratory x-ray image intensifier (XRII) system by comparison with direct measurements of the XRII optical response, showing excellent agreement over the entire frequency range tested (+/- 100 Hz). With proper access to linearized data and continuous fluoroscopy, this method can be implemented in a clinical setting on both XRII and flat-panel detectors. It is shown that the temporal MTF of the CsI-based validation system is a function of exposure rate. The rising-edge response showed more lag than the falling edge, and the temporal MTF decreased with decreasing exposure rate. It is suggested that a small-signal approach, in which the range of exposure rates is restricted to a linear range by using a semitransparent moving edge, would be appropriate for measuring the DQE of these systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".