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Record W2037424018 · doi:10.1063/1.2713229

Automatic navigation of an untethered device in the artery of a living animal using a conventional clinical magnetic resonance imaging system

2007· article· en· W2037424018 on OpenAlexaff
Sylvain Martel, Jean‐Baptiste Mathieu, Ouajdi Felfoul, Arnaud Chanu, Eric Aboussouan, Samer Tamaz, Pierre Pouponneau, L’Hocine Yahia, Gilles Beaudoin, Gilles Soulez, Martin Mankiewicz

Bibliographic record

VenueApplied Physics Letters · 2007
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital Notre-DameUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsMagnetic resonance imagingNuclear magnetic resonanceMaterials scienceBiomedical engineeringComputer scienceMedicineRadiologyPhysics

Abstract

fetched live from OpenAlex

The feasibility for in vivo navigation of untethered devices or robots is demonstrated with the control and tracking of a 1.5mm diameter ferromagnetic bead in the carotid artery of a living swine using a clinical magnetic resonance imaging (MRI) platform. Navigation is achieved by inducing displacement forces from the three orthogonal slice selection and signal encoding gradient coils of a standard MRI system. The proposed method performs automatic tracking, propulsion, and computer control sequences at a sufficient rate to allow navigation along preplanned paths in the blood circulatory system. This technique expands the range of applications in MRI-based interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations357
Published2007
Admission routes1
Has abstractyes

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