MTF and NPS measurements including X-ray scatter - application in a digital mammography system
Bibliographic record
Abstract
Abstract: Purpose: Detector characterization with modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE) inadequately predicts image quality when the imaging system includes patient scatter. This contribution investigates the effect of x-ray scatter on the MTF and NPS in mammography to characterize the system in clinical situations. Material and Methods: A full-field digital mammography unit was characterized at 28 kV Mo/Mo at two beam qualities with and without x-ray scatter. Pre-sampling MTF and NPS without scatter were measured according to the IEC 62220-2 standard. The same imaging parameters were measured again with a mammography test object that simulates x-ray scatter due to the breast. Results: X-ray scatter causes a falloff of MTF (6% at very low spatial frequency (0.1 mm-1) and 9.5% for the rest of the spectra) and adds a constant amount of noise (3% from 0.5 to 2 pl/mm, 6% from 2.5 to 4 pl/mm and 8% from 4.5 pl/mm to the Nyquist frequency). This increase in image noise is related to the amount of scatter integrated by the detector. The reduction of MTF and the increment of noise implies the drop of the DQE at all frequencies. Conclusion: The properties of an image receptor are properly characterized with a scatter free beam. In clinical situations, detectors are used with x-ray scatter from the patient. The use of a test object including scatter to measure MTF and NPS describes the resolution and noise properties of the imaging system with regard to scatter from the patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".