Normalization of the modulation transfer function: The open‐field approach
Bibliographic record
Abstract
The modulation transfer function (MTF) is widely used to describe the spatial resolution of x-ray imaging systems. The MTF is defined to have a zero-frequency value of unity, and it is common practice to ensure this by normalizing a measured MTF curve by the zero-frequency value. However, truncation of the line spread function (LSF) within a finite region of interest (ROI) results in spectral leakage and causes a reduction in the measured MTF zero-frequency value equal to the area of truncated LSF tails. Subsequent normalization by this value may result in inflated MTF values. We show that open-field normalization with the edge method produces accurate MTF values at all nonzero frequencies without need for further normalization by the zero-frequency value, regardless of ROI size. While both normalization techniques are equivalent for a sufficiently large ROI, a 5% inflation in MTF values was observed for a CsI-based flat-panel system when using a 10 cm ROI. Use of open-field normalization avoids potential inflation caused by zero-frequency normalization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".