SU‐E‐I‐86: Using Modulation Transfer Function as a Tool in a Digital Mammography QC Program
Bibliographic record
Abstract
Purpose: To evaluate the efficacy of MTF measurement on digital mammography systems in the Ontario Breast Screening Program (OBSP). Methods: An MTF tool, composed of a copper square on a flexible printed circuit film is supported on top of a 40 mm PMMA slab and imaged with an x‐ray technique appropriate for this thickness. In‐house software, QuickQC, is used to review the image, define regions of interest (ROIs) over each copper edge and calculate the presampled MTF from the line spread function (LSF), by differentiating the edge spread functions (ESF). The noise in the LSF is zero at zero frequency, making the normalization of MTF(0) possible. Since implementing this test in the OBSP 5 years ago, over 180 digital mammography systems have been surveyed semi‐annually (∼ 800 reports). The physicist collects and analyzes the MTF images for all targets and focal spot sizes. MTF results for each digital mammography vendor were summarized to develop pass‐fail criteria. Results: Using appropriate x‐ray techniques, the MTF obtained in this way is similar to values published elsewhere. The noise in the MTF linearly increases to the cutoff spatial frequency, but doesnˈt seriously impair the ability to evaluate compliance. The effects of phosphor glare and extra focal radiation are evident in the low frequency region. Focal spot deterioration is observed as a decrease in MTF compared to the performance for other units of the same model. Underexposure of the phantom causes excessive noise in the MTF. The frequency at which the MTF drops to 50% is typically between 2.5 to 3.5 mm−1 for phosphor based systems with both 50 and 100 um pitch, and 4.0 to 5.0 mm−1 for Se systems with 74–85 um dels. Conclusions: This objective QC test is effective in identifying MTF degradation in a range of different digital mammography systems in the field.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".