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Record W2899322485 · doi:10.1063/1.5052646

A mechanically driven magnetic particle imaging scanner

2018· article· en· W2899322485 on OpenAlexafffund
Hosein Bagheri, Carolyn Kierans, Karyn Nelson, Baldeón Andrade, Claudia Wong, Amy Frederick, M. E. Hayden

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsMagnetic particle imagingExcitationPhysicsMagnetic fieldHarmonicsDecoupling (probability)MagnetOpticsMagnetic particle inspectionMagnetizationMagnetic nanoparticlesVoltageNanoparticleEngineering

Abstract

fetched live from OpenAlex

We describe and demonstrate an approach to magnetic particle imaging in which particle excitation and field free point (FFP) manipulation are decoupled from one another. The additional degrees of freedom enabled by this decoupling suggest alternative strategies for studying and exploiting contrast mechanisms, optimizing image quality and resolution, and device-size scaling. The prototype instrument we describe uses rotating arrays of permanent magnets to scan the FFP through the field of view and current-driven oscillating magnetic fields to elicit non-linear magnetization responses from superparamagnetic nanoparticles. Narrow-band phase sensitive detection of these responses at one or more harmonics of the excitation field provides a rich source of information from which images can be reconstructed. Images generated from data acquired using this instrument are presented, demonstrating the resolution of features with sub-millimetre dimensions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.823

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)0.0000.001

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 head0.006
GPT teacher head0.187
Teacher spread0.182 · 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 designBench or experimental
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

Citations10
Published2018
Admission routes2
Has abstractyes

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