MétaCan
Menu
Back to cohort
Record W2467131512 · doi:10.1097/rli.0000000000000167

Inflection Points in Magnetic Resonance Imaging Technology—35 Years of Collaborative Research and Development

2015· review· en· W2467131512 on OpenAlexaff
Michael L. Wood, Mark A. Griswold, Mark Henkelman, Jürgen Hennig

Bibliographic record

VenueInvestigative Radiology · 2015
Typereview
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMagnetic resonance imagingMagnetComputer scienceExploitPhysics of magnetic resonance imagingk-spaceSoftwareNuclear magnetic resonanceMedical physicsPhysicsEngineeringMagnetic resonance microscopyMechanical engineeringMedicineRadiologyComputer security

Abstract

fetched live from OpenAlex

The technology for clinical magnetic resonance imaging (MRI) has advanced with remarkable speed and in such a manner reflecting the influence of 3 forces-collaboration between disciplines, collaboration between academia and industry, and the enabling of software applications by hardware. The forces are evident in the key developments from the past and emerging trends for the future highlighted in this review article. These developments are associated with MRI system attributes, such as wider, shorter, and stronger magnets; specialty magnets and hybrid devices; k space; and the notion that magnetic field gradients perform a Fourier transform on the spatial distribution of magnetization, phased-array coils and parallel imaging, the user interface, the wide range of contrast possible, and applications that exploit motion-induced phase shifts. An attempt is made to show connections between these developments and how the 3 forces mentioned previously will continue to shape the technology used so productively in clinical MRI.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.994
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
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 head0.133
GPT teacher head0.446
Teacher spread0.313 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInvestigative RadiologySame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207