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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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.394

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 head0.275
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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