Direct conversion detectors: The effect of incomplete charge collection on detective quantum efficiency
Bibliographic record
Abstract
Direct conversion detectors offer the potential for very high resolution and high quantum efficiency for x-ray imaging, however, variations in signal can arise due to incomplete charge collection. A charge transport model was developed to describe the signal and noise resulting from incomplete charge collection. This signal to noise ratio (SNR) reduction was incorporated into the cascaded systems model for a simple x-ray detector. A new excess noise factor, A(c) (termed the collection noise factor) is introduced to describe the reduction in detective quantum efficiency (DQE). The DQE is proportional to the product of the quantum efficiency and the collection noise factor. If the trapping cross sections for electrons and holes are very different, and if the detector is biased improperly, the collection noise factor can drop to as low as 50%. In addition, the signal loss due to incomplete charge collection will reduce the DQE in the presence of added noise. Because of this, the DQE generally does not continue to improve with greater detector thickness. The collection noise factor and DQE are predicted for CdZnTe, PbI2, and Se. The optimization of detector thickness should be based not only on quantum efficiency but also on the charge collection statistics, which are influenced by bias field and polarity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".