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Record W1992503668 · doi:10.1002/mrm.22793

First image from a combined positron emission tomography and field‐cycled MRI system

2011· article· en· W1992503668 on OpenAlexafffund
Geron A. Bindseil, Kyle M. Gilbert, Timothy J. Scholl, William B. Handler, Blaine A. Chronik

Bibliographic record

VenueMagnetic Resonance in Medicine · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Innovation Trust
KeywordsPositron emission tomographyTomographyNuclear medicineBrain positron emission tomographyNuclear magnetic resonanceField (mathematics)PhysicsPreclinical imagingMedicineRadiologyMathematics

Abstract

fetched live from OpenAlex

Combining positron emission tomography and MRI modalities typically requires using either conventional MRI with a MR-compatible positron emission tomography system or a modified MR system with conventional positron emission tomography. A feature of field-cycled MRI is that all magnetic fields can be turned off rapidly, enabling the use of conventional positron emission tomography detectors based on photomultiplier tubes. In this demonstration, two photomultiplier tube-based positron emission tomography detectors were integrated with a field-cycled MRI system (0.3 T/4 MHz) by placing them into a 9-cm axial gap. A positron emission tomography-MRI phantom consisting of a triangular arrangement of positron-emitting point sources embedded in an onion was imaged in a repeating interleaved sequence of ∼1 sec MRI then 1 sec positron emission tomography. The first multimodality images from the combined positron emission tomography and field-cycled MRI system show no additional artifacts due to interaction between the systems and demonstrate the potential of this approach to combining positron emission tomography and MRI.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.871

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)0.0010.000

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 designObservational
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

Citations15
Published2011
Admission routes2
Has abstractyes

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