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Record W1982789128 · doi:10.1118/1.2335491

X-ray imaging with amorphous selenium: Pulse height measurements of avalanche gain fluctuations

2006· article· en· W1982789128 on OpenAlexaff
Brian Lui, Dylan Hunt, A. Reznik, Kenkichi Tanioka, J. A. Rowlands

Bibliographic record

VenueMedical Physics · 2006
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and BioengineeringNational Cancer Institute
KeywordsDetective quantum efficiencyOpticsPhysicsX-ray detectorQuantum efficiencyAvalanche photodiodeX-rayMaterials scienceDetectorImage quality

Abstract

fetched live from OpenAlex

Avalanche multiplication in amorphous selenium (a-Se) can provide a large, adjustable gain for active matrix flat panel imagers (AMFPI), enabling quantum noise limited x-ray imaging during both radiography and fluoroscopy. In the case of direct conversion AMFPI, the multiplication factor for each x ray is a function of its depth of interaction, and the resulting variations in gain can reduce the detective quantum efficiency (DQE) of the system. An experimental method was developed to measure gain fluctuations by analyzing images of individual x rays that were obtained using a video camera with an a-Se target operated in avalanche mode. Pulse height spectra (PHS) of the charge produced per x ray were recorded for monoenergetic 30.9, 49.4, and 73.8 keV x-ray sources. The rapid initial decay and long tail of each PHS can be explained by a model in which positive charge dominates the initiation of avalanche. The Swank information factor quantifies the effect of gain fluctuation on DQE and was calculated from the PHS. The information factor was found to be 0.5 for a 25 microm a-Se layer with a maximum gain of approximately 300. Changing the energy of the incident x ray influenced the range of the primary photoelectron and noticeably affected the tail of the experimental PHS, but did not significantly change the avalanche Swank factor.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.239
Teacher spread0.227 · 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 designObservational
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

Citations15
Published2006
Admission routes1
Has abstractyes

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