Muscle kinematics during isometric contraction: Development of phase contrast and spin tag techniques to study healthy and atrophied muscles
Bibliographic record
Abstract
PURPOSE: To develop and compare phase-contrast (PC) and spin-tag (ST) MR imaging techniques for accurate quantification of velocity and displacement distribution in the muscle tendon complex of the lower leg during isometric contractions under in vivo conditions, in healthy subjects and subjects with atrophy. MATERIALS AND METHODS: Techniques were developed to acquire PC and ST dynamic images, gated to the force exerted by a subject during isometric contraction. Algorithms were optimized for correction of phase shading errors. Flow velocity quantification was validated in phantoms and ex vivo rabbit muscles. Trajectories of pixels calculated from PC images were compared with those in ST images. Velocity distributions were determined in healthy muscles, those atrophied by four weeks of suspension, and during physiotherapy-induced recovery. RESULTS: The technique developed allowed acquisition of images retrospectively gated to the isometric contraction performed with the subject in the scanner. Significant phase shading errors in PC images (approximately 3 cm/second over the field of view) were reduced to the background noise level by the correction algorithm. Tissue trajectories calculated from PC images agreed very well with those from ST images both in human and excised animal tissues. Peak velocities in atrophied muscles were significantly lower compared to the preatrophy state but recovered to baseline values after six weeks of therapy. CONCLUSION: We show the feasibility of monitoring muscle velocity and tissue displacement during voluntary contractions in humans under in vivo conditions using MR tissue motion mapping methods. The clinical feasibility of this technique in monitoring atrophied muscle is also demonstrated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".