Indirect flat-panel detector with avalanche gain
Bibliographic record
Abstract
A new concept - an indirect flat-panel detector with avalanche gain - for low dose x-ray imaging has been proposed. The detector consists of an amorphous selenium (a-Se) photoconductor optically coupled to a structured cesium iodide (CsI) scintillator. Under an electric field ESe, the a-Se is sensitive to light and converts the optical photons emitted from CsI into electronic signal. These signals can be stored and read out in the same fashion as in existing flat-panel detectors. When ESe is increased to > 90 V/μm, avalanche multiplication occurs. The avalanche gain ranges between 1-800 depending on ESe and the thickness of the a-Se layer dSe. The avalanche a-Se photoconductor is referred to as HARP (High-gain Avalanche Rushing amorphous Photoconductor). A cascaded linear system model for the proposed detector was developed in order to determine the optimal CsI properties and avalanche gain for different x-ray imaging applications. Our results showed that x-ray quantum noise limited performance can be achieved at the lowest exposure level necessary for fluoroscopy (0.1 μR) and mammography (0.1 mR) with a moderate avalanche gain of 20 (d = 1-2 μm). A laboratory test system using an existing HARP tube optically coupled (through a lens) to a CsI layer was built and the advantage of avalanche gain in overcoming electronic noise was demonstrated experimentally. One of the advantages of the avalanche gain is that it will permit the use of high resolution (HR) CsI (which due to its low light output has not previously been used in flat-panel detectors) to improve DQE at high spatial frequencies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".