MétaCan
Menu
Back to cohort
Record W3011677336 · doi:10.1117/12.2549546

Response of CZT pixels to parallel and oblique x rays

2020· article· en· W3011677336 on OpenAlexaff
Robert J. LeClair, Emily L. McCarthy

Bibliographic record

VenueMedical Imaging 2020: Physics of Medical Imaging · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPhysicsPixelOpticsPhotonDetectorIsotropyDot pitchEnergy (signal processing)

Abstract

fetched live from OpenAlex

CZT detectors are gaining popularity in the medical field. A numerical study of the response of CZT detector pixels to parallel and oblique x rays was conducted. The average k-fluorescence energy generated in a given pixel is 26.0 keV. It is assumed that such x rays travel a mean free path mfp = 0.161 mm in CZT before being absorbed. For applications in nuclear medicine, incident 140 keV photons were used. Each pixel had an area 1.5 mm x 3.3 mm and thickness 5 mm. The spacing between pixels was &Delta; = 0:3 mm and since &Delta; &gt;mfp, the model predicts no cross talk between pixels. For parallel photons, the probability of escape is PF<sub>esc</sub> = 4:0% and the quantum efficiency <i>&eta;</i> = 0:88. Next consider an isotropic source located at a distance 20 cm from the detector plane. For incident angles of 0&deg; to 23&deg; <i>&eta;</i> varied from 0.88 to 0.5 and the fluorescence escape from 3.8 % to 2.0 %. For breast CT applications a typical pixel size is 0.2 mm by 0.2 mm and thickness 0.75 mm. The corresponding values for photons of energy 60 keV are as follows: (i) parallel <i>&eta;</i> = 0:94, PF<sub>esc</sub> = 30%, oblique <i>&eta;</i> = 0:94, PF<sub>esc</sub> = 30% at 0&deg;; <i>&eta;</i> = 0:55, PF<sub>esc</sub> = 18% at 23&deg;. This work provides some quantification of the response of CZT detector pixels to x rays. Cross talk would need to be examined for the breast CT detector.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.264
Teacher spread0.256 · 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 designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMedical Imaging 2020: Physics of Medical ImagingSame topicAdvanced X-ray and CT ImagingFrench-language works237,207