Longitudinal relaxation in fat‐water mixtures and its dependence on fat content at 3 T
Bibliographic record
Abstract
Longitudinal (T1) relaxation of triglyceride molecules and water is of interest for fat‐water separation and fat quantification. A better understanding of T1 relaxation could benefit modeling for applications in fat quantification and relaxation mapping. This work investigated T1 relaxation of spectral resonances of triglyceride molecules and water in liquid fat‐water mixtures and its dependence on the fat fraction. Dairy cream and a safflower oil emulsion were used. These were diluted with distilled water to produce a variety of fat mass fractions (4.4% to 35% in dairy cream and 6.3% to 52.3% in safflower oil emulsion). T1 was measured at room temperature at 3 T using an inversion recovery STimulated Echo Acquisition Mode (STEAM) MR spectroscopy method with a series of inversion times. T1 variations as a function of fat fraction were investigated for various resonances. A two‐component model was developed to describe the relaxation in a fat‐water mixture as a function of the fat fraction. The T1 of water and of all fat resonances studied in this work decreased as the fat fraction increased. The relative variation in T1 was different for each fat resonance. The T1 of the methylene resonance showed the least variation as a function of the fat fraction. The proposed two‐component model closely fits the observed T1 variations. In conclusion, this work clarifies how the T1 of major and minor fat resonances and of the water resonance varies as a function of the fat fraction in fat‐water mixtures. Knowledge of these variations could serve modeling, analysis of MRI measurements in fat‐water mixtures, and phantom preparation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".