Image quality of direct conversion detectors for mammography and radiography: a theoretical comparison
Bibliographic record
Abstract
Direct conversion detectors have the potential to provide very high resolution and high detective quantum efficiency (DQE). Selection of a material that is appropriate for the task is dictated by the material properties. A linear cascaded systems analysis of DQE is used to predict the performance of several detector materials such as amorphous Se, CdZnTe, and PbI2. A model is used to predict the spatial frequency-dependent DQE(f) for each material. This model includes: (1) x-ray absorption, (2) K fluorescence, (3) conversion gain, and (4) incomplete charge collection. A depth-dependent approach is used to account for gain variations and charge transport characteristics that change throughout the detector. In the model a parallel cascade, and non-elementary stages are used to model the effect of K-fluorescence reabsorption followed by incomplete charge collection. The DQE(f) is determined across an x-ray energy range of 10 to 100 keV for each material under typical bias conditions ranging from 0.1 V/μm to 10 V/μm. K-fluorescence escape and reabsorption blurring can cause marked reductions in the DQE(f). It is further reduced by incomplete charge collection which can theoretically decrease the DQE(f) by as much as 50% in extreme situations. This model will help determine key factors that will influence material selection for direct conversion x-ray 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".