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Centric SPRITE MRI of Biomaterials with Short<i>T</i><sub>2</sub>*

2012· reference-entry· en· W1838504231 on OpenAlexafffund
Igor V. Mastikhin, Bruce J. Balcom

Bibliographic record

VenueEncyclopedia of Magnetic Resonance · 2012
Typereference-entry
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaSiberian Branch, Russian Academy of Sciences
KeywordsSprite (computer graphics)Encoding (memory)Computer sciencePoint (geometry)Signal-to-noise ratio (imaging)Single pointComputer visionOpticsArtificial intelligenceNuclear magnetic resonancePhysicsMathematics

Abstract

fetched live from OpenAlex

Single-point imaging techniques are capable of performing 1H and 31P imaging of short T 2 * tissues with a good signal-to-noise ratio (SNR) and free of geometric distortions, making possible MRI of objects with very large susceptibility differences. Centric sampling strategies implemented in the SPRITE (single point ramped imaging with T 1-enhancement) family of sequences lead to significantly reduced acquisition times and facilitate the combination of the imaging sequences with various magnetization preparation schemes. Single-point imaging techniques are slower than frequency-encoding-based methods; however, they can allow the imaging of species with ultrashort T 2 * with good spatial accuracy and well-described signal intensity, thus enabling quantitative analysis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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.

Study designNot applicable
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

Citations7
Published2012
Admission routes2
Has abstractyes

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