Detective quantum efficiency of a silicon microstrip photon-counting detector having edge-on geometry under mammography imaging condition
Bibliographic record
Abstract
We have investigated the image quality of a silicon microstrip detector system operated in single-photon counting mode under mammography imaging condition. The detector has an edge-on geometry with a tilting angle of 5 degrees to the normal direction of X-ray incidence. It is composed of four modules and each module employs 256 silicon microstrips. Using a slanted-edge knife technique, the modulation-transfer function (MTF) without aliasing was determined. Noise-power spectrum (NPS) was determined using two-dimensional (2D) Fourier analysis on the line-scanned 2D images. Based on the measured MTF and NPS results, detective quantum efficiency (DQE) was calculated. These systematic procedures were repeated at various energy thresholds. Asymmetric MTF properties between two perpendicular directions were observed because of the scan motion. Spectral densities in NPS were white for spatial frequencies. The best DQE value around zero-spatial frequency was about 0.7. It was observed that the DQE was independent of the level of X-ray exposure, which is desirable for low-dose mammography.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".