Sci‐Sat AM(1): Imaging‐07: Open field normalization: How to avoid inflation to MTF and DQE values caused by zero‐frequency normalization
Bibliographic record
Abstract
PURPOSE: To show that the novel open-field normalization technique prevents a common error in calculation of the detective quantum efficiency (DQE) caused by zero-frequency normalization of the modulation transfer function (MTF). METHOD AND MATERIALS: Models describing zero-frequency and open-field normalization were used to derive the resulting measured MTF, noise power spectrum (NPS) and DQE using a finite region of interest (ROI) of image data. Simulated one-dimensional images containing Gaussian blur were used to model a deterministic system and to calculate the resultant values. Measurements were made using both zero-frequency and open-field normalization with ROIs ranging in size from 1-10 cm. RESULTS: Use of a finite ROI results in truncation of the system line-spread function (LSF) causing the zero-frequency value of the measured MTF to be less than the true MTF value of unity, and causes spectral leakage in both the MTF and NPS. Zero-frequency normalization of the MTF inflates values at all non-zero frequencies. Since no zero-frequency normalization is performed on the NPS, this causes inflated DQE values. Simulated results show a 6% inflation of DQE values for a ROI of 10 cm, which increases as the ROI is reduced. Open-field normalization accurately determines MTF and NPS (and thus DQE) values at all frequencies away from zero frequency. CONCLUSION: Open-field normalization measurements provide a good estimate of the true MTF and DQE. This approach should be used to avoid a common error in DQE calculations that is not obvious and inflates DQE calculations by 5-20%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".