Performance of solid-state image sensors in medical X-ray applications
Bibliographic record
Abstract
During the last decade the conventional film screen in medical X-ray equipment is being replaced by digital detectors. These digital detectors consist of an energy converter, x-ray to photons or x-ray to electric charge, a gain element and a read-out device, e.g. CCD or CMOS detector. In this tutorial the focus will be on indirect X-ray detection using a phosphorous applied on a fiber optic plate coupled to a CCD or CMOS detector. The influence of each separate element on the final image quality will be discussed. In medical X-ray imaging the detective quantum efficiency (DQE) is an important parameter for defining the image quality and system performance. It is a measure of how well a detector is able to extract information from a beam of radiation. For high doses – in the quantum-noise limited regime – the DQE is independent of dose, but at low doses the DQE starts to decrease and hence it is a good indicator of the system performance at low radiation levels. The method to measure DQE will be explained. The effect of the performance of a solid-state image sensor on the DQE will be presented. The main items to discuss are the noise, quantum efficiency, charge capacity, pixel size and the binning feature. The presentation will end with a summary of the strengths and weaknesses of solid-state image sensors for medical X-ray applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".