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Record W4200038152 · doi:10.1002/mp.15411

The detective quantum efficiency of cadmium telluride photon‐counting x‐ray detectors in breast imaging applications

2021· article· en· W4200038152 on OpenAlexafffund
James Day, Jesse Tanguay

Bibliographic record

VenueMedical Physics · 2021
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetective quantum efficiencyCharge sharingPhysicsOpticsDetectorX-ray detectorPhotonFigure of meritNoise (video)Photon countingDot pitchCadmium telluride photovoltaicsPixelOptoelectronicsImage qualityComputer scienceImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract Purpose In breast imaging applications, cadmium telluride (CdTe) photon counting x‐ray detectors (PCDs) may reduce radiation dose and enable single‐shot multi‐energy x‐ray imaging. The purpose of this work is to determine the upper limits of the detective quantum efficiency (DQE) of CdTe PCDs for x‐ray mammography and to compare them with the published DQEs of energy‐integrating detectors (EIDs) and other PCDs. Methods We calibrated and validated a Monte Carlo (MC) model of the DQE of CdTe PCDs using an XCounter CdTe PCD. Our model accounted for charge sharing, electronic noise, and charge summation logic. We used a 28 kVp Mo/Mo spectrum hardened by 3.9 cm of Lucite to optimize the detector thickness and energy threshold for pixel sizes of 50, 85, and 100 m with and without inter‐pixel charge summation logic. The figure of merit used for optimization was the integral of the DQE, which is equivalent to the detectability index for a delta function task function, which represents a high‐frequency task. Results For an electronic noise level equal to that of the XCounter, the optimal DQE(0) without charge summing was 0.74. Charge summing for charge‐sharing correction reduced DQE(0) by 14% due to an increase in electronic noise. Reducing the electronic noise to ∼0.5 keV per pixel in combination with charge summing resulted in DQE(0) 0.78 for 85 m pixels, which is approximately equal to that of a ‐Se and slot‐scanning silicon‐strip PCDs. At higher spatial frequencies, and for matched pixel sizes, the DQE was inferior to that of a ‐Se EIDs and superior to that of slot‐scanning silicon‐strip PCDs in the scan direction but inferior in the slit direction. Conclusions (1) CdTe PCDs have the potential to provide a zero‐frequency DQE equal to that of a ‐Se EIDs and slot‐scanning silicon‐strip PCDs, but this will require electronic noise levels ∼0.5 keV per pixel. (2) At mid‐to‐high spatial frequencies the DQE of CdTe PCDs may be (a) inferior to that of a ‐Se EIDs and slot‐scanning silicon‐strip PCDs in the slit direction, and (b) superior to slot‐scanning silicon‐strip PCDs in the scan direction.

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 score0.884
Threshold uncertainty score0.445

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 head0.005
GPT teacher head0.244
Teacher spread0.238 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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