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Record W2054335197 · doi:10.1002/mrm.22487

Transverse relaxometry with stimulated echo compensation

2010· article· en· W2054335197 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 · 2010
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche Médicale
KeywordsMultisliceRelaxometrySpin echoNuclear magnetic resonancePulse sequenceSIGNAL (programming language)PhysicsCoherence (philosophical gambling strategy)Adiabatic processTransverse planeComputer scienceMagnetic resonance imagingMedicineRadiology

Abstract

fetched live from OpenAlex

Presented is a fitting model for transverse relaxometry data acquired with the multiple-refocused spin-echo sequence. The proposed model, requiring no additional data input or pulse sequence modifications, compensates for imperfections in the transmit field and radiofrequency (RF) profiles. Exploiting oscillatory echo behavior to estimate alternate coherence pathways, the model compensates for prolonged signal decay from stimulated echo pathways yielding precise monoexponential T(2) quantification. Verified numerically and experimentally at 4.7 T in phantoms and the human brain, over 95% accuracy is readily attainable in realistic imaging situations without sacrificing multislice capabilities or requiring composite or adiabatic RF pulses. The proposed model allows T(2) quantitation in heterogeneous transmit fields and permits thin refocusing widths for efficient multislice imaging.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.306
Teacher spread0.292 · 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