Impact of Lubberts Effect on Amorphous Selenium Indirect Conversion Avalanche Detector for Medical X-Ray Imaging
Bibliographic record
Abstract
The exponential X-ray absorption makes the indirect conversion X-ray image sensors vulnerable to the Lubberts effect, which in turn makes the sensor more sensitive to the electronic noise. A cascaded linear-system model is proposed to find the required electric field to overcome the effect of electronic noise and depth dependent X-ray absorption (Lubberts effect) in amorphous selenium indirect conversion avalanche detectors. The model also includes scattering due to K-fluorescence reabsorption. The effect of depth dependent X-ray absorption is more pronounced in thicker detectors. It is observed that, at the Nyquist frequency (fN) of 2.5 mm-1, the presampling modulation transfer function of CsI deteriorates from 0.75 to 0.1 due to Lubberts effect in a CsI layer having thickness of 0.6 mm. The detective quantum efficiency (DQE) at fN (2.5 mm-1) drops from 0.037 to 0.01 at a field of 60 V/μm due to Lubberts effect. The Lubberts fraction decreases with increasing the field thereafter. The avalanche gain enhances the signal strength and improves the frequency dependent DQE(f) by overcoming the Lubberts effect and as well as the effect of the electronic noise. An avalanche gain of 45 is sufficient to overcome the effect of the electronic noise.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".