Considerations for Gradient Echo Echo Planar Imaging of Skeletal Muscle
Bibliographic record
Abstract
Adequate blood circulation to muscles is important for good health. In recent years, researchers have increasingly used magnetic resonance imaging (MRI) to study temporal skeletal muscle physiological changes using gradient echo (GRE) echo planar imaging (EPI). These studies, typically involving exercise or ischemic challenges, have differentiated healthy subjects from athletic or unhealthy populations, such as those with peripheral vascular disease. The T*2-weighted GRE EPI signal is sensitive to changes in blood flow and oxygenation in muscle. Furthermore, the signal can be weighted differently by adjusting the echo time (TE), achieving either blood volume (BV) or blood oxygenation level dependent (BOLD) imaging. This paper comprehensively reviews the muscle GRE EPI literature to date, with a particular emphasis on studies that have also used other modalities in an attempt to elucidate the GRE EPI signal characteristics. Finally, the systemic and muscle physiological factors that are thought to influence the GRE EPI signal during and after exercise are described. These include temperature, blood flow, blood oxygen saturation, myoglobin, and postural effects. The summary attempts to predict, based on the published literature, the temporal dynamics of these signal-influencing parameters in a hypothetical exercise experiment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".