Performance of amorphous selenium based unipolar charge sensing detector for photon-counting X-ray imaging
Bibliographic record
Abstract
Practical photon counting detectors that have been adopted for commercial use are typically based on crystalline or polycrystalline materials. However, these types of materials are challenging to scale to large-area medical imaging applications because of yield and cost issues associated with the crystal growth and bonding technology required to interface the sensor with the readout IC. An alternate approach is to use a large-area-compatible, mature, direct conversion X-ray-detection sensor such as amorphous selenium (a-Se). The technical challenges for photon counting with a-Se lie in overcoming (1) the slow carrier-transport material property of a-Se, which leads to count-rate limitations due to pile-up, and (2) the lower X-ray-to-charge conversion gain, which degrades SNR and can be resolved by improved design of pixel readout circuits. In this paper, we address the a-Se material limitation by leveraging a unipolar charge sensing detector design. We demonstrate that the proposed unipolar charge sensing detector provides an effective method to detect charge of the polarity type having a higher mobility-lifetime product, obviating the need for detection of the opposite polarity slow transport charge. Transient signal measurements indicate that a quasi depth independent signal rise-time is achieved with the unipolar charge sensing detector. Moreover, two orders of magnitude improvement is observed compared to the conventional a-Se detector rise-time (0.15 μs vs. 25 μs).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".