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Record W2061349600 · doi:10.1002/jmri.21029

Neuro MR: Principles

2007· review· en· W2061349600 on OpenAlexaff
Timothy P. L. Roberts, David J. Mikulis

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2007
Typereview
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSpin echoContrast (vision)Nuclear magnetic resonanceArtifact (error)Computer scienceContext (archaeology)Image contrastMagnetic resonance imagingImage qualityPhysicsMedicineArtificial intelligenceRadiologyImage (mathematics)

Abstract

fetched live from OpenAlex

The principles of contrast mechanisms and fast pulse sequences underlying neurological and neuroradiological application of MRI are introduced in this part of a two-part review. In particular, the relaxation time constants T(1), T(2), and T(2) (*) are introduced, along with pulse sequences harnessing their contrast, spin-echo, fast spin echo (FSE), inversion recovery (IR), gradient recalled echo (GRE), and echo planar imaging (EPI). The use of gadolinium (Gd)-based contrast is discussed in both T(1)- and T(2) (*)-weighted sequences. Tradeoffs between speed and image quality are discussed, particularly in the context of long echo train sequences (FSE imaging and EPI). The influence of parallel imaging strategies is also discussed. T(2) (*) sensitivity is discussed as both a source of artifact as well as a contrast mechanism (perfusion imaging with contrast agents, susceptibility weighted imaging [SWI], and blood oxygenation level dependent [BOLD] imaging). Finally, the contrast mechanism of diffusion is introduced, as well as the concept of anisotropy. From these principles, the other part of this two-part review draws upon the pulse sequences and contrast mechanisms to design disease and indication-specific protocols for state-of-the-art clinical use.

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: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.402
Teacher spread0.330 · 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
GenreReview

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

Citations64
Published2007
Admission routes1
Has abstractyes

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