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Record W3196583500 · doi:10.1002/mp.15194

A detective quantum efficiency for spectroscopic X‐ray imaging detectors

2021· article· en· W3196583500 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsRedlen Technologies (Canada)University of VictoriaToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetective quantum efficiencyDetectorOpticsPhysicsImage qualityComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Purpose Spectroscopic X‐ray detectors (SXDs) are under development for X‐ray imaging applications. Recent efforts to extend the detective quantum efficiency (DQE) to SXDs impose a barrier to experimentation and/or do not provide a task‐independent measure of detector performance. The purpose of this article is to define a task‐independent DQE for SXDs that can be measured using a modest extension of established DQE‐metrology methods. Methods We defined a task‐independent spectroscopic DQE and performed a simulation study to determine the relationship between the zero‐frequency DQE and the ideal‐observer signal‐to‐noise ratio (SNR) of low‐frequency soft‐tissue, bone, iodine, and gadolinium signals. In our simulations, we used calibrated models of the spatioenergetic response of cadmium telluride (CdTe) and cadmium–zinc–telluride (CdZnTe) SXDs. We also measured the zero‐frequency DQE of a CdTe detector with two energy bins and of a CdZnTe detector with up to six energy bins for an RQA9 spectrum and compared with model predictions. Results The spectroscopic DQE accounts for spectral distortions, energy‐bin‐dependent spatial resolution, interbin spatial noise correlations, and intrabin spatial noise correlations; it is mathematically equivalent to the squared SNR per unit fluence of the generalized least‐squares estimate of the height of an X‐ray impulse in a uniform noisy background. The zero‐frequency DQE has a strong linear relationship with the ideal‐observer SNR of low‐frequency soft‐tissue, bone, iodine, and gadolinium signals, and can be expressed in terms of the product of the quantum efficiency and a Swank noise factor that accounts for DQE degradation due to, for example, charge sharing (CS) and electronic noise. The spectroscopic Swank noise factor of the CdTe detector was measured to be 0.81 0.04 and 0.83 0.04 with and without anticoincidence logic for CS suppression, respectively. The spectroscopic Swank noise factor of the CdZnTe detector operated with four energy bins was measured to be 0.82 0.02 which is within 5% of the theoretical value. Conclusions The spectroscopic DQE defined here is (1) task‐independent, (2) can be measured using a modest extension of existing DQE‐metrology methods, and (3) is predictive of the ideal‐observer SNR of soft‐tissue, bone, iodine, and gadolinium signals. For CT applications, the combination of CS and electronic noise in CdZnTe spectroscopic detectors will degrade the zero‐frequency DQE by 10 %–20 % depending on the electronic noise level and pixel size.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.630

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 head0.006
GPT teacher head0.242
Teacher spread0.236 · 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