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Record W2163910747 · doi:10.1109/tbme.2012.2229709

A Magnetic-Resonance-Imaging-Compatible Remote Catheter Navigation System

2012· article· en· W2163910747 on OpenAlexafffund
M. Ali Tavallaei, Yogesh Thakur, S. Haider, Maria Drangova

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2012
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsVancouver Coastal HealthWestern University
FundersCanadian Institutes of Health Research
KeywordsScannerMagnetic resonance imagingCatheterRotation (mathematics)Computer scienceRotary encoderUltrasonic motorSIGNAL (programming language)Real-time MRIAcousticsComputer visionBiomedical engineeringPhysicsSimulationEncoderArtificial intelligenceEngineeringRadiologyMedicineVibration

Abstract

fetched live from OpenAlex

A remote catheter navigation system compatible with magnetic resonance imaging (MRI) has been developed to facilitate MRI-guided catheterization procedures. The interventionalist's conventional motions (axial motion and rotation) on an input catheter - acting as the master - are measured by a pair of optical encoders, and a custom embedded system relays the motions to a pair of ultrasonic motors. The ultrasonic motors drive the patient catheter (slave) within the MRI scanner, replicating the motion of the input catheter. The performance of the remote catheter navigation system was evaluated in terms of accuracy and delay of motion replication outside and within the bore of the magnet. While inside the scanner bore, motion accuracy was characterized during the acquisition of frequently used imaging sequences, including real-time gradient echo. The effect of the catheter navigation system on image signal-to-noise ratio (SNR) was also evaluated. The results show that the master-slave system has a maximum time delay of 41 ± 21 ms in replicating motion; an absolute value error of 2 ± 2° was measured for radial catheter motion replication over 360° and 1.0 ± 0.8 mm in axial catheter motion replication over 100 mm of travel. The worst-case SNR drop was observed to be 2.5%.

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)0.001
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.260
Teacher spread0.249 · 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

Citations39
Published2012
Admission routes2
Has abstractyes

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