Advanced MR Imaging Techniques for Skeletal Muscle Evaluation
Bibliographic record
Abstract
Diagnostic imaging procedures for muscle evaluation have typically provided basic information concerning gross anatomical change resulting from pathology. Up until recently the musculoskeletal radiologist has been fairly limited to using simple proton-density weighted fat-saturated and short tau inversion recovery magnetic resonance imaging scans for assessment of skeletal muscle. Recent advances, however, have resulted in development of newer scans and postprocessing methods that provide much more than gross muscle structure. Scans providing fine structure, muscle function, and metabolism can easily be done using clinical scanners. Here we describe how diffusion tensor imaging (DTI) and blood oxygenation level-dependent (BOLD) imaging together can provide detailed information on muscle structural and functional changes. DTI is useful for visualizing muscle tears, and BOLD can be used for vascular insufficiency (e.g., compartment syndrome). In clinical sites that are gaining experience using these techniques, imaging of muscle pathology is becoming increasingly thorough. In the future, these methods will reduce the need for invasive approaches to study muscle pathology.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".