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Record W2109077450 · doi:10.1002/mrm.22081

Evaluation of a positron emission tomography (PET)‐compatible field‐cycled MRI (FCMRI) scanner

2009· article· en· W2109077450 on OpenAlexafffund
Kyle M. Gilbert, Timothy J. Scholl, William B. Handler, Jamu K. Alford, Blaine A. Chronik

Bibliographic record

VenueMagnetic Resonance in Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Innovation Trust
KeywordsScannerMagnetPositron emission tomographyResistive touchscreenDetectorMaterials scienceOpticsPhysicsTomographyNuclear magnetic resonanceNuclear medicineComputer scienceComputer vision

Abstract

fetched live from OpenAlex

Field-cycled MRI (FCMRI) uses two independent, actively controlled resistive magnets to polarize a sample and to provide the magnetic field environment during data acquisition. This separation of tasks allows for novel forms of contrast, reduction of susceptibility artifacts, and a versatility in design that facilitates the integration of a second imaging modality. A 0.3T/4-MHz FCMRI scanner was constructed with a 9-cm-wide opening through the side for the inclusion of a photomultiplier-tube-based positron emission tomography (PET) system. The performance of the FCMRI scanner was evaluated prior to integrating PET detectors. Quantitative measurements of the system's signal, phase, and temperature were recorded. The polarizing and readout magnets could be operated continuously at 100 A without risk of damage to the system. Transient instabilities in the readout magnet, caused by the pulsing of the polarizing magnet, dissipated in 50 ms; this resulted in a steady-state homogeneity of 32 Hz over a 7-cm-diameter volume. The short- and long-term phase behaviors of the readout field were sufficiently stable to prevent visible readout or phase-encode artifacts during imaging. Preliminary MR images demonstrated the potential of the FCMRI scanner and the efficacy of integrating a PET system.

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 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 score0.917
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.354
Teacher spread0.333 · 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

Citations18
Published2009
Admission routes2
Has abstractyes

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