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Record W2970502598 · doi:10.1109/trpms.2019.2937678

A Novel Amorphous Selenium Avalanche Detector Structure for Low Dose Medical X-Ray Imaging

2019· article· en· W2970502598 on OpenAlexafffund
Salman M. Arnab, M. Z. Kabir

Bibliographic record

VenueIEEE Transactions on Radiation and Plasma Medical Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetective quantum efficiencyAvalanche photodiodeOptoelectronicsMaterials scienceX-ray detectorDetectorOpticsNoise (video)Physics

Abstract

fetched live from OpenAlex

A novel amorphous selenium (a-Se) avalanche detector structure for low dose direct-conversion flat-panel X-ray detector is proposed. The proposed structure contains blocking layers to reduce carrier injection from metal electrodes and hole trapping layer to separate X-ray absorption layer from avalanche gain region. The feasibility of the structure for avalanche gain with negligible avalanche noise is studied by using the semiconductor module of COMSOL multiphysics together with a cascaded linear system. The model considers carrier injection from electrodes and charge carrier transport through various layers of multilayer a-Se structure in order to analyze the transient and steady-state electric field distribution across the detector. A cascaded linear system model that includes reabsorption of K-fluorescent X-rays, carrier trapping in bulk and trapping layer, and avalanche multiplication of charge carrier is used to calculate the frequency-dependent detective quantum efficiency [DQE(f)] and modulation transfer function (MTF) of the proposed structure. The avalanche gain enhances the signal strength and improves the DQE(f) by overcoming the effect of electronic noise at low X-ray doses. The structure is applied for breast tomosynthesis and observed that the proposed structure offers the required avalanche gain to ensure quantum noise limited operation at reduced exposures.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.256
Teacher spread0.245 · 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 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

Citations8
Published2019
Admission routes2
Has abstractyes

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