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Record W2069339299 · doi:10.1063/1.3202410

The integration of real and virtual magnetic resonance imaging experiments in a single instrument

2009· article· en· W2069339299 on OpenAlexafffund
Jonathan C. Sharp, Donghui Yin, Richard H. Bernhardt, Qunli Deng, Andrew E. Procca, Randy L. Tyson, Kan Lo, Bogusław Tomanek

Bibliographic record

VenueReview of Scientific Instruments · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsNational Research Council Institute for Biodiagnostics
FundersWestern Economic Diversification Canada
KeywordsMagnetic resonance imagingComputer scienceSystem integrationVirtual instrumentMagnetic fieldNuclear magnetic resonanceSimulationData acquisitionPhysicsOperating system

Abstract

fetched live from OpenAlex

We present the design of an integrated system for performing both real and virtual (simulated) magnetic resonance imaging (MRI) experiments. We emphasize the approaches used to maximize the level of integration and also the benefits that tight real-virtual integration brings for a scientific instrument. The system has been implemented for both low field (0.2 T) and high field (9.4 T) imaging systems. The simulations can run for any MRI experiment and we demonstrate the operation of the system for T(1), T(2), T(2) ( *), and diffusion contrasts.

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 score0.920
Threshold uncertainty score0.235

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 head0.027
GPT teacher head0.334
Teacher spread0.306 · 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

Citations14
Published2009
Admission routes2
Has abstractyes

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