Characterization of T<sub>1</sub> bias in skeletal muscle from fat in MOLLI and SASHA pulse sequences: Quantitative fat‐fraction imaging with T<sub>1</sub> mapping
Bibliographic record
Abstract
Purpose To characterize the effects of fat on commonly used T1 mapping sequences and evaluate a new method of quantitative fat fraction (FF) imaging for low fractions based on the modulation of T1 values by the fat pool. Methods Bloch equation simulations and phantom and in vivo (skeletal muscle) experiments were used to characterize the response of the modified Look–Locker inversion recovery (MOLLI) and saturation recovery single‐shot acquisition (SASHA) T1 mapping sequences to fat–water systems with known FFs (0%–10%) at 1.5T. FFs were measured with single voxel spectroscopy and Dixon imaging methods. A new T1‐based FF imaging method was evaluated using Monte Carlo simulations and phantom and in vivo experiments. Results SASHA and MOLLI had similar T1 dependence on FF, with characteristic under‐ or overestimation of T1 values as a function of off‐resonance frequency (30–70 ms variation in native T1 per 1% FF). FF maps generated from the SASHA method yielded a low variability of ±0.25% for a signal‐to‐noise ratio of 150:1 in the nonsaturation image, with good agreement with spectroscopy and a performance that is superior to that of Dixon methods at low FFs. Conclusion Fat results in negative or positive shifts in native tissue T1 measured with MOLLI and SASHA over a narrow range of off‐resonance frequencies; T1 shifts from fat can be used to accurately quantify FF. Magn Reson Med 77:237–249, 2017. © 2016 Wiley Periodicals, Inc.
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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 | 0.002 |
| 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 | 0.000 |
| 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".