Method of measuring NEQ as a quality control metric for digital mammography
Bibliographic record
Abstract
PURPOSE: Current quality control protocols for digital mammography rely on subjective assessments of image quality or simple measures that are not comparable between vendor platforms. The noise-equivalent quanta (NEQ) can be expressed in units of image quanta (fluence) for the spatial frequency range of interest, enabling comparisons between systems and x-ray spectra. The purpose of this work is to explore use of a simple phantom to measure the components of the noise-equivalent quanta of digital mammography systems for use in routine quality control. METHODS: A simple phantom is imaged on six mammography systems from different vendors. The phantom contains uniform regions for measurement of noise power spectrum (NPS), slanted edges for measurement of modulation transfer function (MTF), and objects of various thicknesses for measurement of contrast. Images were acquired at a range of dose levels on each system to examine how measurements scale with dose, and multiple images were taken at a single dose point to examine measurement reproducibility. RESULTS: The phantom and measurement methods show good reproducibility, with average coefficient of variation values of less than or equal to 15% on all systems evaluated. Measured MTF and NPS values are comparable to other published results when the increase in scattered radiation generated by placing the phantom on the breast support is accounted for. CONCLUSIONS: Measurement of the parameters required to calculate NEQ from a single image of a simple phantom is practical, and shows promise as a method of evaluating image quality for routine quality control of digital mammography 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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".