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Record W4238915296 · doi:10.1109/iembs.2006.4397762

Ferromagnetic Artifacts in MRI: Minimization of Motion Effects in Long TR Acquisitions

2006· article· en· W4238915296 on OpenAlexaff
Eric Aboussouan, Sylvain Martel

Bibliographic record

VenueConference proceedings · 2006
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMinificationComputer scienceMotion (physics)FerromagnetismComputer visionArtificial intelligencePhysicsCondensed matter physics

Abstract

fetched live from OpenAlex

A feasibility study is under way for a multiplexed tracking/imaging/propulsion sequence to control a ferromagnetic microdevice in the human vasculature. Ferromagnetic artifact motion can be problematic for the acquired images but we show that when the phase encoding direction is made to match the main direction of motion of the device, the acquisition of planes distant of 3.4 cm of the ferromagnetic bead used show acceptable distortions and signal loss even when the bead incurs significant motion during acquisition. At approximately 6.4 cm, no distortion or signal loss is observed. These results suggest that slow breathing motion can be measured and/or gated using the undistorted parts of the images

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)0.004
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.275
Teacher spread0.263 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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