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Record W2139168168 · doi:10.1088/1748-0221/6/12/c12006

Detective quantum efficiency of a silicon microstrip photon-counting detector having edge-on geometry under mammography imaging condition

2011· article· en· W2139168168 on OpenAlexaff
Sangwoon Yun, H K Kim, Hanbean Youn, Ong Siong Chiew Joe, S Kim, J Park, Dongseok Kang, Young Hun Sung, J. Marchal, Jesse Tanguay, Ian A. Cunningham

Bibliographic record

VenueJournal of Instrumentation · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsDetective quantum efficiencyOptical transfer functionOpticsPhysicsDetectorAliasingImage qualityPhotonSpatial frequencyMammographyFilter (signal processing)Computer scienceImage (mathematics)

Abstract

fetched live from OpenAlex

We have investigated the image quality of a silicon microstrip detector system operated in single-photon counting mode under mammography imaging condition. The detector has an edge-on geometry with a tilting angle of 5 degrees to the normal direction of X-ray incidence. It is composed of four modules and each module employs 256 silicon microstrips. Using a slanted-edge knife technique, the modulation-transfer function (MTF) without aliasing was determined. Noise-power spectrum (NPS) was determined using two-dimensional (2D) Fourier analysis on the line-scanned 2D images. Based on the measured MTF and NPS results, detective quantum efficiency (DQE) was calculated. These systematic procedures were repeated at various energy thresholds. Asymmetric MTF properties between two perpendicular directions were observed because of the scan motion. Spectral densities in NPS were white for spatial frequencies. The best DQE value around zero-spatial frequency was about 0.7. It was observed that the DQE was independent of the level of X-ray exposure, which is desirable for low-dose mammography.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.230
Teacher spread0.221 · 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 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

Citations4
Published2011
Admission routes1
Has abstractyes

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