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Record W2088161976 · doi:10.1002/jmri.20315

An improved method for spinal functional MRI with large volume coverage of the spinal cord

2005· article· en· W2088161976 on OpenAlexafffund
Patrick W. Stroman, Jennifer Kornelsen, Jane M. Lawrence‐Dewar

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2005
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of ManitobaNational Research Council Institute for BiodiagnosticsQueen's University
FundersCanada Research Chairs
KeywordsSpinal cordSagittal planeMedicineVoxelCordAnatomyMagnetic resonance imagingNeuroanatomyBiomedical engineeringNuclear medicineRadiologySurgery

Abstract

fetched live from OpenAlex

PURPOSE: To develop a spinal functional MRI (fMRI) method with three-dimensional coverage of a large extent of the spinal cord with minimal partial volume effects MATERIALS AND METHODS: fMRI data of the cervical spinal cord were obtained at 1.5 T with a single-shot fast spin-echo imaging method, from thin contiguous sagittal slices spanning the cord. Thermal stimulation was applied to the palm of the hand in a block pattern with 15 degrees C for stimulation and 32 degrees C during baseline periods. Prior to analysis, the image data at each time point were reformatted into three-dimensional volumes and resliced perfectly transverse to the spinal cord. Smoothing was applied only in the superior-inferior (S/I) direction across uniform tissue types. Active voxels were then identified by means of a correlation to a model paradigm. RESULTS: The resulting activity maps demonstrate activity primarily in ipsilateral sensory areas and in some motor areas, consistent with the spinal cord neuroanatomy. These data also demonstrate detail of the subsegmental organization of the spinal cord, as well as anatomical detail of the spinous processes and positions of nerve roots. CONCLUSION: The spinal fMRI method described enables large volume coverage of the spinal cord in three dimensions, with reliable and reproducible results.

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: Methods
Teacher disagreement score0.950
Threshold uncertainty score0.326

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 head0.014
GPT teacher head0.340
Teacher spread0.326 · 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

Citations59
Published2005
Admission routes2
Has abstractyes

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