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Record W2145945239 · doi:10.1002/mrm.10661

Determination of optimal angles for variable nutation proton magnetic spin‐lattice, <i>T</i><sub>1</sub>, and spin‐spin, <i>T</i><sub>2</sub>, relaxation times measurement

2003· article· en· W2145945239 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMagnetic Resonance in Medicine · 2003
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
FundersHeart and Stroke Foundation of Canada
KeywordsNutationSteady-state free precession imagingPhysicsNuclear magnetic resonanceSpin (aerodynamics)PrecessionWeightingRelaxation (psychology)Range (aeronautics)Computational physicsCondensed matter physicsMaterials scienceMagnetic resonance imagingQuantum mechanicsThermodynamics

Abstract

fetched live from OpenAlex

T1 and T2 can be rapidly determined with a combination of multiangle spoiled gradient recalled echo (SPGR) and steady-state free precession (SSFP) imaging. Previously, we demonstrated a simple method for determining the set of SPGR and SSFP angles that provided greater T1 and T2 precision than a set of uniformly spaced angles. In this article a more rigorous approach for determining angles is described. Weighted least-squares is also introduced for T1 and T2 estimation and a novel weighting function described. This new approach, suited for imaging applications where large T1 and T2 ranges are anticipated, provides high and uniform precision over a wide range of T1 and T2 values.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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