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Record W2149128412 · doi:10.1259/dmfr/91373164

Impact of ambient lighting intensity and duration on the signal-to-noise ratio of images from photostimulable phosphor plates processed using DenOptix<sup>®</sup>and ScanX<sup>®</sup>systems

2004· article· en· W2149128412 on OpenAlexaff
Rajesh Ramamurthy, CF Canning, James P. Scheetz, AG Farman

Bibliographic record

VenueDentomaxillofacial Radiology · 2004
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOpticsScannerPhosphorSignal-to-noise ratio (imaging)Materials scienceNuclear medicineNoise (video)HistogramStandard deviationMathematicsPhysicsComputer scienceArtificial intelligenceOptoelectronicsMedicineStatistics

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the impact on photostimulable phosphor (PSP) image signal-to-noise ratio (SNR) of pre-scanning ambient lighting exposures. METHOD: PSP imaging plates (IPs) were exposed to different radiation exposures to achieve flat field images. The exposed IPs were subjected variously to visible light of different intensities (300, 150 or 20 lux) for durations ranging from < 10 s to 120 s. They were processed using laser scanners from two systems for further comparison (DenOptix versus ScanX). Histogram analysis was performed in each case and mean pixel value and its standard deviation were used as surrogates to assess SNR. Statistical methods applied included analysis of variance with Tukey honestly significant difference test for pair wise comparisons. The a priori alpha was set at P < or = 0.05. RESULTS: SNR decreased with increased duration and intensity of pre-scanning light exposure. Lower X-ray exposures resulted in decreased signal resulting in reduced SNR, and increased the need to reduce ambient lighting. No statistically significant differences were found comparing ScanX and DenOptix digital imaging systems in terms of SNR. CONCLUSION: Reduced ambient lighting is preferred for handling IPs prior to processing in the laser scanner.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2004
Admission routes1
Has abstractyes

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