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Record W2132543539 · doi:10.1109/iembs.2004.1403342

Adapting MRI systems to propel and guide microdevices in the human blood circulatory system

2005· article· en· W2132543539 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic resonance imagingComputer sciencePath (computing)BiomagnetismMedical imagingHuman bodyComputer visionArtificial intelligenceBiomedical engineeringEngineeringMagnetic fieldPhysicsMedicineRadiology

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) systems are widely used to gather noninvasively images of the interior of the human body. This paper suggests that an MRI system can be seen beyond being just a tool for imaging purpose but one that can propel and guide special microdevices in the human body to perform specific medical tasks. More specifically, an MRI system can potentially be used to image the region of interest, propel a microdevice through the generation of magnetic gradients, determine the location of the device, compute the corrective actions through feedback control algorithms and adjust the generation of the magnetic gradients accordingly to navigate such a microdevice in a preplanned path. This paper presents an introductory description of the proposed techniques, the main issues to consider, and some preliminary data indicating the validity of this approach.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.347

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 head0.012
GPT teacher head0.208
Teacher spread0.195 · 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

Quick stats

Citations48
Published2005
Admission routes2
Has abstractyes

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