Screen optics effects on detective quantum efficiency in digital radiography: Zero‐frequency effects
Bibliographic record
Abstract
Indirect flat panel imagers have been developed for digital radiography, fluoroscopy and mammography, and are now in clinical use. Screens made from columnar structured cesium iodide (CsI) scintillators doped with thallium have been used extensively in these detectors. The purpose of this article is to investigate the effect of screen optics, e.g., light escape efficiency versus depth, on gain fluctuation noise, expressed as the Swank factor. Our goal is to obtain results useful in optimizing screens for digital radiography systems. Experimental measurements from structured CsI samples were used to derive their screen optics properties, and the same methods can also be applied to powder screens. CsI screens, all of the same thickness but with different optical designs and manufacturing techniques, were obtained from Hamamatsu Photonics Corporation. The pulse height spectra (PHS) of the screens were measured at different x-ray energies. A theoretical model was developed for the light escape efficiency and a method for deriving light escape efficiency versus depth from experimental PHS measurements was implemented and applied to the CsI screens. The results showed that the light escape efficiency varies essentially linearly as a function of depth in the CsI samples, and that the magnitude of variation is relatively small, leading to a high Swank factor.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".