A non-invasive, 3D, dynamic MRI method for measuring muscle moment arms in vivo: Demonstration in the human ankle joint and Achilles tendon
Bibliographic record
Abstract
Muscle moment arms are used widely in biomechanical analyses. Often they are measured in 2D or at a series of static joint positions. In the present study we demonstrate a simple MRI method for measuring muscle moment arms dynamically in 3D from a single range-of-motion cycle. We demonstrate this method in the Achilles tendon for comparison with other methods, and validate the method using a custom apparatus. The method involves registration of high-resolution joint geometry from MRI scans of the stationary joint with low-resolution geometries from ultrafast MRI scans of the slowly moving joint. Tibio-talar helical axes and 3D Achilles tendon moment arms were calculated throughout passive rotation for 10 adult subjects, and compared with recently published data. A simple validation was conducted by comparing MRI measurements with direct physical measurements made on a phantom. The moment arms measured using our method and those of others were similar and there was good agreement between physical measurements (mean 41.0mm) and MRI measurements (mean 39.5mm) made on the phantom. This new method can accurately measure muscle moment arms from a single range-of-motion cycle without the need to control rotation rate or gate the scanning. Supplementary data includes custom software to assist implementation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".