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Record W4241449978 · doi:10.1109/nssmic.2001.1008670

Design considerations for efficient binary megavoltage photon detector structures

2005· article· en· W4241449978 on OpenAlexfundno aff
H. Keller, R. Hinderer, M. Glass, R. Jeraj, R. Schmidt, J. Kapatoes, T.R. Mackie

Bibliographic record

Venue2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310) · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsDetectorMonte Carlo methodBinary numberPhotonImage resolutionQuantum efficiencyComputer scienceElectronic engineeringDetective quantum efficiencyOpticsPhysicsEngineeringMathematicsArtificial intelligenceImage quality

Abstract

fetched live from OpenAlex

In this work, Monte Carlo methods are used for the design of highly-efficient detector structures for megavoltage X-ray imaging. The detector structures consist of a converter material and an active medium ("binary" detector systems). The novel approach is to impose a spatial structure on the converter material which intersperses the active medium and defines a specified cell size. The dimensions of these structures have to be optimized with respect to efficiency and spatial resolution. The results show, that the efficiency (quantum efficiency and detective quantum efficiency at zero frequency) of such structures surpasses the efficiency of currently available detector technologies by far. The efficiency depends on the dimensions of the converter structure and the active medium as well as the materials itself. In general, larger converter structures result in a higher efficiency. However, the size of the structures are limited by the specifications for the desired spatial resolution.

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)0.000
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 score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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
Published2005
Admission routes1
Has abstractyes

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