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Record W2112512591 · doi:10.1118/1.1713298

Lag measurement in an active matrix flat‐panel imager

2004· article· en· W2112512591 on OpenAlexafffund
C. Schroeder, T. Stanescu, S Rathee, B. G. Fallone

Bibliographic record

VenueMedical Physics · 2004
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsResidualActive matrixPhotonLagDetectorPhoton energyMaterials scienceSIGNAL (programming language)PhysicsNuclear medicineElectrodeThin-film transistorMathematics

Abstract

fetched live from OpenAlex

Lag and residual contrast have been quantified in an amorphous selenium (a-Se) active-matrix flat-panel imager (AMFPI) as a function of frame time, kilovoltage (kV) and megavoltage (MV) x-ray photon energies and amount of radiation incident on the detector. The AMFPI contains a 200 microm thick a-Se layer deposited on a thin film transistor (TFT) array of size 8.7 cm x 8.7 cm with an 85-microm pixel pitch. For all energies, the lag (signal normalized to the signal due to exposure) for the first (n = 1) and second (n = 2) frame after exposure ranges from 0.45% to 0.91% and from 0.29% to 0.51%, respectively. The amount of lag was determined to be a function of the time after the x-ray exposure irrespective of frame time or the magnitude of exposure. The lag for MV photon energies was slightly less than that for kV photon energies. The residual contrast for all energies studied ranges from 0.41% to 0.75% and from 0.219% to 0.41% for the n = 1 and n = 2 frames, respectively. These results show that lag and residual contrast in kV and MV radiographic applications are always less than 1% for the detection system used and only depend on the time after x-ray 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.485

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 head0.035
GPT teacher head0.298
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations18
Published2004
Admission routes2
Has abstractyes

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