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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".