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Record W1978040980 · doi:10.1118/1.3622599

Characterization of the deflections of a catheter steered using a magnetic resonance imaging system

2011· article· en· W1978040980 on OpenAlexafffund
Frédérick P. Gosselin, Viviane Lalande, Sylvain Martel

Bibliographic record

VenueMedical Physics · 2011
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsSPHERESDipoleScannerFerromagnetismCatheterMagnetic dipoleMaterials scienceMagnetic resonance imagingNuclear magnetic resonancePhysicsOpticsAcousticsCondensed matter physicsRadiologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: The authors quantify the deflections of a catheter and a guidewire in MR setting with different designs of ferromagnetic tips and a system of high gradient coils which can generate gradients, and thus forces, 20 times larger than a conventional scanner. METHODS: Different designs of catheter tips are experimentally tested in an effort to maximize the deflections. One to two ferromagnetic spheres are attached at the distal tip of the catheter (or guidewire) with different spacing between the spheres. The effect of dipole-dipole interaction on the steering of the catheter is studied through experimentation and theoretical modeling. The effect of using many spheres on the artefact generated in fast imaging sequences is also investigated. RESULTS: A catheter and a guidewire are successfully steered by applying magnetic gradients inside a magnetic resonance scanner. More ferromagnetic material allows for larger magnetic forces, however, the use of two ferromagnetic spheres introduces undesired dipole-dipole interactions. Two ferromagnetic spheres generate a single larger artefact as they are close together. CONCLUSIONS: By varying the distance between the two ferromagnetic spheres, a balance can be struck between the need to minimize the size of the tip and the undesirable dipole-dipole interaction.

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: none
Teacher disagreement score0.571
Threshold uncertainty score0.150

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)0.0000.000

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 head0.032
GPT teacher head0.288
Teacher spread0.257 · 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

Citations52
Published2011
Admission routes2
Has abstractyes

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