Bibliographic record
Abstract
In the recent years, we have been witnessing a very dynamic development of diagnostic methods of imaging. In contemporary radiology, the carrier of the diagnostic information is the image, obtained as a result of an X-ray beam transmitted through the patient's body, with modulation of intensity, and processing of data collected by the detector. Depending on the diagnostic method used, signals can be detected with analog (x-ray film) or digital systems (CR, DR and DDR). Each of these methods of image acquisition, due to its own technological solutions, determines a different quality of imaging (diagnostic data). The introduction of digital image receptors, instead of conventional SF systems, increased the patient dose, as a result of a gradually increasing exposure. This followed from the fact that in digital systems, the increased radiation dose reduces image noise and improves image quality, and that is owing to the data capacity of these systems (impossible in SF systems with a limited data capacity of the image detector). The availability of the multitude of imaging systems, each characterized by disparate qualitative and quantitative parameters, implies the problem of evaluation and enforcement of a proper efficiency from manufacturers of these systems.At the same time, there is a legal problem present in our country, i.e. the lack of laws and regulations regarding standards of the scope of quality control (parameters) and measurement methodology for the systems of digital image acquisition. In the European countries, the scope and standards of control are regulated by the manufacturers and European Guidelines, whereas in the United States, AAPM Reports have been introduced, that specifically describe methods of tests performance, their frequency, as well as target values and limits. This paper is a review of both, the scope of quality control parameters of image detectors in analog and digital systems of imaging, and the measurement methodology. The parameters determining the image quality are as follows: detection efficiency, dynamic range, spatial sampling, contrast resolution, spatial resolution, noise, and quantitative detection efficiency. Validation of the measurement methods, establishing standards of radiographic techniques for the performed examinations, and creating a uniform system of supervision, appears to be the only way to ensure an effective control of imaging systems and to eliminate an increasing exposure.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".