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Record W2122631012 · doi:10.1002/jmri.23642

Physics of MRI: A primer

2012· review· en· W2122631012 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2012
Typereview
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMagnetic resonance imagingNuclear magnetic resonancePhysicsPresentation (obstetrics)Physics of magnetic resonance imagingPerspective (graphical)Computer scienceMedical physicsSpin echoRelaxometryRadiologyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

This article is based on an introductory lecture given for the past many years during the "MR Physics and Techniques for Clinicians" course at the Annual Meeting of the ISMRM. This introduction is not intended to be a comprehensive overview of the field, as the subject of magnetic resonance imaging (MRI) physics is large and complex. Rather, it is intended to lay a conceptual foundation by which magnetic resonance image formation can be understood from an intuitive perspective. The presentation is nonmathematical, relying on simple models that take the reader progressively from the basic spin physics of nuclei, through descriptions of how the magnetic resonance signal is generated and detected in an MRI scanner, the foundations of nuclear magnetic resonance (NMR) relaxation, and a discussion of the Fourier transform and its relation to MR image formation. The article continues with a discussion of how magnetic field gradients are used to facilitate spatial encoding and concludes with a development of basic pulse sequences and the factors defining image contrast.

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.

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 score0.695

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 head0.046
GPT teacher head0.378
Teacher spread0.332 · 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