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Record W2278994996 · doi:10.1002/nbm.3454

Volumetric navigated MEGA‐SPECIAL for real‐time motion and shim corrected GABA editing

2015· article· en· W2278994996 on OpenAlexaff
Muhammad G. Saleh, Ali Alhamud, Jamie Near, André van der Kouwe, Ernesta M. Meintjes

Bibliographic record

VenueNMR in Biomedicine · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMedical Research CouncilNational Institutes of Health
KeywordsShim (computing)SubtractionImaging phantomComputer scienceMotion (physics)Rotation around a fixed axisArtificial intelligenceBiomedical engineeringPhysicsNuclear magnetic resonanceComputer visionChemistryMaterials scienceNuclear medicineOpticsMathematicsMedicine

Abstract

fetched live from OpenAlex

Mescher-Garwood (MEGA) editing with spin echo full intensity acquired localization (MEGA-SPECIAL, MSpc) is a technique to acquire γ-aminobutyric acid (GABA) without macromolecule (MM) contamination at a TE of 68 ms. However, due to the requirement of multiple shot-localization, it is often susceptible to subject motion and B0 inhomogeneity. A method is presented for real-time shim and motion correction (ShMoCo) using volumetric navigators to correct for motion and motion-related B0 inhomogeneity during MSpc acquisition. A phantom experiment demonstrates that ShMoCo restores the GABA peak and improves spectral quality in the presence of motion and zero- and first-order shim changes. The ShMoCo scans were validated in three subjects who performed up-down and left-right head rotations. Qualitative assessment of these scans indicates effective reduction of subtraction artefacts and well edited GABA peaks, while quantitative analysis indicates superior fitting and spectral quality relative to scans with no correction. Copyright © 2015 John Wiley & Sons, Ltd.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.341
Teacher spread0.310 · 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 designNot applicable
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

Citations25
Published2015
Admission routes1
Has abstractyes

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