Transverse Strains in Muscle Fascicles during Voluntary Contraction: A 2D Frequency Decomposition of B-Mode Ultrasound Images
Bibliographic record
Abstract
When skeletal muscle fibres shorten, they must increase in their transverse dimensions in order to maintain a constant volume. In pennate muscle, this transverse expansion results in the fibres rotating to greater pennation angle, with a consequent reduction in their contractile velocity in a process known as gearing. Understanding the nature and extent of this transverse expansion is necessary to understand the mechanisms driving the changes in internal geometry of whole muscles during contraction. Current methodologies allow the fascicle lengths, orientations, and curvatures to be quantified, but not the transverse expansion. The purpose of this study was to develop and validate techniques for quantifying transverse strain in skeletal muscle fascicles during contraction from B-mode ultrasound images. Images were acquired from the medial and lateral gastrocnemii during cyclic contractions, enhanced using multiscale vessel enhancement filtering and the spatial frequencies resolved using 2D discrete Fourier transforms. The frequency information was resolved into the fascicle orientations that were validated against manually digitized values. The transverse fascicle strains were calculated from their wavelengths within the images. These methods showed that the transverse strain increases while the longitudinal fascicle length decreases; however, the extent of these strains was smaller than expected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".