Muscle activity with 0.5 T upright MRI—DESS to measure <i>T</i><sub>2</sub> in biceps and triceps
Bibliographic record
Abstract
Abstract The purpose of this study was to determine if muscle activity of the biceps followed by isometric flexion changes T2 measured in the biceps. It is hypothesized that an increase in T2 will be observed in the biceps but not in the triceps after flexion exercise. Ten healthy volunteers were imaged with a one‐channel neck coil while seated in a 0.5 T upright open magnetic resonance imaging (MRI) scanner using a three‐dimensional double echo steady‐state (DESS) sequence. Volunteers were imaged while relaxing their arm for 10, 20, and 30 min during an isometric biceps flexion immediately following performance of biceps curls to exhaustion, and again after relaxing for 10 and 20 min. Voxel‐wise T2 was calculated by fitting to a DESS signal equation in regions segmented at muscle centers to determine mean T2. During isometric biceps flexion immediately following biceps curls, mean T2 increased (average 33%, p < 0.05) in the biceps but not in the triceps. By 20 min after curls, mean T2 decreased (p < 0.05), and was near preactivity values. In contrast, there was no change in triceps T2 across any activity or postactivity time points. Intra‐rater repeatability was excellent (ICC: 0.90–0.97). This study demonstrated that measuring T2 in an active muscle is feasible using a DESS sequence in an upright open MRI scanner. This could enable the study of muscle function while the muscle is working and weight‐bearing, rather than of the “fatigue” of the muscles after activity. In comparison to electromyography, MRI also enables the study of deep muscles and allows simultaneous assessment of activity and function.
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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 | 0.000 |
| 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".