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Record W1535025660 · doi:10.5772/19847

Analog and Digital Systems of Imaging in Roentgenodiagnostics

2011· article· en· W1535025660 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInTech eBooks · 2011
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsImage qualityScope (computer science)DetectorQuality (philosophy)Digital imagingComputer scienceDigital imageEnforcementData acquisitionNoise (video)Digital radiographyMedical physicsComputer visionArtificial intelligenceImage processingData scienceMedicineImage (mathematics)TelecommunicationsPolitical sciencePhysicsRadiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.246
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it