Evaluation of Magnetization Transfer Contrast Sequences: Application to Monitor Age-Related Differences in Muscle Macromolecular Fraction
Bibliographic record
Abstract
Background/Objectives: Several sequences for magnetization transfer contrast (MTC) imaging are available, from indices of MTC ranging from quantitative magnetization transfer (qMT) that yields the macromolecular fraction to simple ratios of signal intensities with and without a magnetization transfer (MT) pulse. Aging muscle undergoes changes including an increase in fibrosis and adipose accompanied by fiber atrophy and loss. The objective is to evaluate five MTC sequences to study age-related differences in muscle tissue composition. Methods: The lower leg (calf) of 15 young (8M/7F, 25.8 ± 3.7 years) and 9 senior subjects (5F/4M, 68.4 ± 3.3 years) was imaged with the following sequences: multi-offset qMT fit to the Ramani and Yarnykh models, single-offset qMT two-parameter fit to the Ramani model, a semi-quantitative MTsat sequence, magnetization transfer ratio (MTR), and MTR-corrected (MTRcorr) for B1 inhomogeneities. T1 mapping was also performed. Statistical analysis was performed to identify significant age-related and regional (intermuscular) differences. Results: Significant age-related decreases (p < 0.001) in macromolecular fraction (from two-parameter fit), MTsat, MTR, and MTRcorr were identified. A significant age-related increase in T1 (p < 0.001) was also identified. Pearson correlation coefficients between T1 and MTC indices were weak to moderate but significant. Conclusions: Age-related decreases in MTC may reflect that loss of myofibrillar proteins dominates the increase in collagen content with age. Further, the modest correlation of MTC indices with T1 indicates that all the age-related differences in MTC cannot be explained by an increase in inflammation. The MTsat sequence was identified as the most clinically relevant in terms of acquisition speed, post-processing simplicity, and ability to identify age-related differences in macromolecular fractions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".