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Record W2105149720 · doi:10.1109/tbme.2008.919720

Real-Time MRI-Based Control of a Ferromagnetic Core for Endovascular Navigation

2008· article· en· W2105149720 on OpenAlexafffund
Samer Tamaz, Richard Gourdeau, Arnaud Chanu, Jean‐Baptiste Mathieu, Sylvain Martel

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsPolytechnique MontréalCARE Canada
FundersInstitut de Cardiologie de Montréal
KeywordsPulsatile flowPID controllerTrajectoryComputer scienceController (irrigation)BeadFerromagnetismReal-time MRIBiomedical engineeringRobotControl theory (sociology)Computer visionSimulationMagnetic resonance imagingArtificial intelligenceMaterials scienceEngineeringControl engineeringPhysicsRadiologyControl (management)MedicineTemperature control

Abstract

fetched live from OpenAlex

This paper shows that even a simple proportional-integral-derivative (PID) controller can be used in a clinical MRI system for real-time navigation of a ferromagnetic bead along a predefined trajectory. Although the PID controller has been validated in vivo in the artery of a living animal using a conventional clinical MRI platform, here the rectilinear navigation of a ferromagnetic bead is assessed experimentally along a two-dimensional (2D) path as well as the control of the bead in a pulsatile flow. The experimental results suggest the likelihood of controlling untethered microdevices or robots equipped with a ferromagnetic core inside complex pathways in the human body.

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: none
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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations126
Published2008
Admission routes2
Has abstractyes

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