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Record W2229922317 · doi:10.1002/cmr.b.20011

An inductive method to measure mechanical excitation spectra for MRI elastography

2004· article· en· W2229922317 on OpenAlexaff
Donald B. Plewes, C. Luginbuhl, Christopher K. Macgowan, Ingolf Sack

Bibliographic record

VenueConcepts in Magnetic Resonance Part B · 2004
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsHospital for Sick ChildrenHealth Sciences CentreUniversity of TorontoSickKids FoundationSunnybrook Health Science Centre
FundersErnst Schering Foundation
KeywordsElastographyAcousticsNonlinear systemExcitationHarmonicsHarmonicActuatorDetectorMagnetic resonance elastographyPhysicsStiffnessMeasure (data warehouse)WaveformComputer scienceOpticsUltrasoundArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Harmonic MR elastography (MRE) monitors the propagation of acoustic waves in tissues in the audio regime. Oscillatory motions with large amplitudes can induce nonlinear wave propagation effects resulting in harmonics that evolve over space. In order to understand these effects, knowledge of the motions of applied mechanical motion is needed to rule out the presence of harmonic motion arising from the mechanical source. We propose a simple technique to measure the spectral content of mechanical excitation based on the use of a set of detection coils mounted on the elastography excitation system. The motion of these coils causes a small signal to be induced from the applied static magnetic field of the MRI system. A detailed analysis shows that quantitative assessment of excitations is possible with correct geometrical arrangement of the detector coils. However, it shows that nonlinear effects can also occur depending on the alignment of the detection coils with respect to Bo. The system is easy to operate and allows for the time resolved observation of the actuator motion for each experimental setup. The system is intended to be used before and after MRE experiments to determine excitation spectral content and repeatability. We demonstrate its use in a one‐dimensional elastography experiment and show that this information is an essential prerequisite for studying material nonlinear elastic properties using MRE. © 2004 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 21B: 32–39, 2004

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.718

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 head0.015
GPT teacher head0.282
Teacher spread0.268 · 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 designOther design
Domainnot available
GenreMethods

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

Citations6
Published2004
Admission routes1
Has abstractyes

Explore more

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