SU‐GG‐BRC‐06: An Enabling Technology for Very Low Exposure X‐Ray Imaging
Bibliographic record
Abstract
Purpose: Medical procedures such as cardiac catheterization, angiography and the deployment of endovascular devices are routinely performed using x‐ray fluoroscopy, in which each image is obtained at very low x‐ray exposures. The imaging performance of current solid‐state flat panel detectors (FPD) is compromised by electronic noise at these low detector exposures (0.1–10 μR/frame). There is thus a clear need to develop an imaging detector with the quantum noise limited operation of an x‐ray image intensifier and the inherent advantages of a compact solid‐state device. Here we propose a technology that takes advantage of avalanche multiplication of charge in an amorphous selenium (a‐Se) photoconductor. Method and Materials: To determine whether this technology holds promise for next‐generation FPDs, we investigate the following: (1) device and material requirements for prevention of electrical breakdown, (2) leakage currents at high electric fields, (3) real‐time imaging capability and linearity, and (4) the compliance of an avalanche a‐Se photoconductor with low‐voltage image readout electronics. Results: Our results show that a distributed resistive layer coupled to the avalanche photoconductor enables breakdown‐free operation. We report, for the first time, avalanche gains exceeding 104 in a solid‐state x‐ray detector, and leakage currents of only ∼10 pA/mm2. The detector has a voltage‐programmable avalanche gain and can be operated in a linear regime at 30 frames per second over a five order of magnitude x‐ray exposure range, including the lowest clinical exposures encountered in fluoroscopy. Furthermore it is compatible with existing thin film transistor technology on which current FPDs are based. Conclusion: This detector technology should enable the development of next‐generation dose‐efficient FPDs for interventional radiology as well as advanced applications such as cone‐beam computed tomography or tomosynthesis. Combined with techniques such as region‐of‐interest fluoroscopy, our detector technology could significantly reduce radiation dose to the patient and physician.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".