Scintillator high‐gain avalanche rushing photoconductor active‐matrix flat panel imager: Zero‐spatial frequency x‐ray imaging properties of the solid‐state SHARP sensor structure
Bibliographic record
Abstract
PURPOSE: The authors are investigating the feasibility of a new type of solid-state x-ray imaging sensor with programmable avalanche gain: scintillator high-gain avalanche rushing photoconductor active matrix flat panel imager (SHARP-AMFPI). The purpose of the present work is to investigate the inherent x-ray detection properties of SHARP and demonstrate its wide dynamic range through programmable gain. METHODS: A distributed resistive layer (DRL) was developed to maintain stable avalanche gain operation in a solid-state HARP. The signal and noise properties of the HARP-DRL for optical photon detection were investigated as a function of avalanche gain both theoretically and experimentally, and the results were compared with HARP tube (with electron beam readout) used in previous investigations of zero spatial frequency performance of SHARP. For this new investigation, a solid-state SHARP x-ray image sensor was formed by direct optical coupling of the HARP-DRL with a structured cesium iodide (CsI) scintillator. The x-ray sensitivity of this sensor was measured as a function of avalanche gain and the results were compared with the sensitivity of HARP-DRL measured optically. The dynamic range of HARP-DRL with variable avalanche gain was investigated for the entire exposure range encountered in radiography∕fluoroscopy (R∕F) applications. RESULTS: The signal from HARP-DRL as a function of electric field showed stable avalanche gain, and the noise associated with the avalanche process agrees well with theory and previous measurements from a HARP tube. This result indicates that when coupled with CsI for x-ray detection, the additional noise associated with avalanche gain in HARP-DRL is negligible. The x-ray sensitivity measurements using the SHARP sensor produced identical avalanche gain dependence on electric field as the optical measurements with HARP-DRL. Adjusting the avalanche multiplication gain in HARP-DRL enabled a very wide dynamic range which encompassed all clinically relevant medical x-ray exposures. CONCLUSIONS: This work demonstrates that the HARP-DRL sensor enables the practical implementation of a SHARP solid-state x-ray sensor capable of quantum noise limited operation throughout the entire range of clinically relevant x-ray exposures. This is an important step toward the realization of a SHARP-AMFPI x-ray flat-panel imager.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".