MO‐AB‐BRA‐07: Low Dose Imaging with Avalanche Amorphous Selenium Flat Panel Imager
Bibliographic record
Abstract
Purpose: We present the first active matrix flat panel imager (AMFPI) capable of producing x‐ray quantum noise limited images at low doses by overcoming the electronic noise through signal amplification by photoconductive avalanche gain (gav). The indirect detector fabricated uses an optical sensing layer of amorphous selenium (a‐Se) known as High‐Gain Avalanche Rushing Photoconductor (HARP). The detector design is called Scintillator HARP (SHARP)‐AMFPI. This is the first image sensor to utilize solid‐state HARP technology. Methods: The detector's electronic readout is a 24 × 30 cm 2 array of thin film transistors (TFT) with a pixel pitch of 85 µm. The HARP structure consists of a 15 µm layer of a‐Se isolated from the high voltage (HV) and signal electrode by a 2 µm thick hole blocking layer and electron blocking layer, respectively, to reduce dark current. A 150 µm thick structured CsI scintillator with reflective backing and a fiber optic faceplate (FOP) was coupled to the semi‐transparent HV bias electrode of the HARP structure. Images were acquired using a 30 kVp Mo/Mo spectrum typically used in mammography. Results: Optical sensitivity measurements demonstrate that gav = 76 ± 5 can be achieved over the entire active area of the detector. At a constant dose to the detector of 6.67 µGy, image quality increases with gav until the effective electronic noise is negligible. Quantum noise limited images can be obtained with doses as low as 0.18 µGy. Conclusion: We demonstrate the feasibility of utilizing avalanche gain to overcome electronic noise. The indirect detector fabricated is the first solid‐state imaging sensor to use HARP, and the largest active area HARP sensor to date. Our future work is to improve charge transport within the HARP structure and utilize a transparent HV electrode.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".