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Record W2022514814 · doi:10.1002/mrm.20605

T<sub>1</sub>, T<sub>2</sub> relaxation and magnetization transfer in tissue at 3T

2005· article· en· W2022514814 on OpenAlexaff
Greg J. Stanisz, Ewa Odrobina, Joseph Pun, Michael Escaravage, Simon J. Graham, Michael Bronskill, R. Mark Henkelman

Bibliographic record

VenueMagnetic Resonance in Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsHospital for Sick ChildrenHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMagnetization transferRelaxation (psychology)Nuclear magnetic resonanceMagnetizationPulse sequenceWhite matterMagnetic relaxationSpin–lattice relaxationChemistryPhysicsMagnetic resonance imagingMagnetic fieldBiologyMedicine

Abstract

fetched live from OpenAlex

T1, T2, and magnetization transfer (MT) measurements were performed in vitro at 3 T and 37 degrees C on a variety of tissues: mouse liver, muscle, and heart; rat spinal cord and kidney; bovine optic nerve, cartilage, and white and gray matter; and human blood. The MR parameters were compared to those at 1.5 T. As expected, the T2 relaxation time constants and quantitative MT parameters (MT exchange rate, R, macromolecular pool fraction, M0B, and macromolecular T2 relaxation time, T2B) at 3 T were similar to those at 1.5 T. The T1 relaxation time values, however, for all measured tissues increased significantly with field strength. Consequently, the phenomenological MT parameter, magnetization transfer ratio, MTR, was lower by approximately 2 to 10%. Collectively, these results provide a useful reference for optimization of pulse sequence parameters for MRI at 3 T.

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 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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.275
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

Citations1,249
Published2005
Admission routes1
Has abstractyes

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