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Record W1562488399 · doi:10.1002/mrm.25729

Biexponential longitudinal relaxation in white matter: Characterization and impact on T<sub>1</sub> mapping with IR‐FSE and MP2RAGE

2015· article· en· W1562488399 on OpenAlexaff
James Rioux, Ives R. Levesque, Brian K. Rutt

Bibliographic record

VenueMagnetic Resonance in Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsNuclear magnetic resonanceRelaxation (psychology)Magnetization transferWhite matterChemistryNuclear medicineMagnetic resonance imagingPhysicsMedicineRadiology

Abstract

PURPOSE: Magnetization transfer in white matter (WM) causes biexponential relaxation, but most quantitative T1 measurements fit data assuming monoexponential relaxation. The resulting monoexponential T1 estimate varies based on scan parameters and represents a source of variation between studies, especially at high fields. In this study, we characterized WM T1 relaxation and performed simulations to determine how to minimize this deviation. METHODS: To characterize biexponential relaxation, four volunteers were scanned at 3T and 7T using inversion recovery fast spin echo (IR-FSE) with 13 inversion times (TIs). Three volunteers were scanned with IR-FSE using TIs chosen by simulations to reduce T1 deviation, and with MP2RAGE. RESULTS: At 3T, the biexponential relaxation has a short component of T1 = 48 ms (9%) and a long component of T1 = 939 ms. At 7T the short component is T1 = 57 ms (11%) and the long component is 1349 ms (89%). For IR-FSE, acquiring four TIs with a minimum of 150 ms (3T) or 200 ms (7T) yielded monoexponential T1 estimates that match the long component to within 10 ms. For MP2RAGE, significant differences (90 ms at 3T, 125 ms at 7T) remain at all parameter values. CONCLUSION: Many T1 mapping sequences yield robust estimates of the long T1 component with suitable choice of TIs, allowing reproducible, sequence-independent T1 values to be measured. However, this is not true of MP2RAGE in its current implementation. Magn Reson Med 75:2265-2277, 2016. © 2015 Wiley Periodicals, Inc.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: medium

MRI physics study of biexponential T1 relaxation; measurement reproducibility in the assay sense, not the reproducibility of research.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

The study investigates MRI relaxation measurements and sequence parameters, not research reproducibility as a social or methodological practice.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

MRI physics characterizing biexponential T1 relaxation to improve mapping is imaging-method development, not study of research practice.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations63
Published2015
Admission routes1
Has abstractyes

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