Magnetic Resonance Imaging in Human Body Composition Research: From Quantitative to Qualitative Tissue Measurement
Bibliographic record
Abstract
Incremental improvements in our knowledge of human body composition are abetted by advances in research technology. Indeed, magnetic resonance imaging (MRI) represents a technological advance that has profoundly influenced body composition research. Routine applications of MRI include the measurement of whole-body and regional adipose tissue distribution, quantification of lean tissue and its principal constituent skeletal muscle, and the measurement of visceral adipose tissue. MRI is now the method of choice for calibration of field methods designed to measure body fat and skeletal muscle in vivo. Common to these applications is the measurement of tissue quantity. More recently proton (1H) and sodium (23Na) MRI protocols have been developed that measure the quality (lipid and sodium concentration) of skeletal muscle tissue. These unique applications of MRI represent a major advance in the study of altered muscle composition in vivo, with numerous applications in both applied and clinical medicine. In this review we provide a brief overview of routine applications of MRI in body composition research, followed by a focus on more recent applications of MRI that employ fast-imaging sequences for qualitative measurement of human skeletal muscle.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".