Use Of Diffusion Tensor Magnetic Resonance Imaging For Assessment Of Musculoskeletal Structure Following High-force Eccentric Exercise: A Case Study
Bibliographic record
Abstract
Typically, only dramatic muscle damage associated with edema and hemorrhage has been visible with MRI as even with MRI's superior spatial resolution, subtle muscle tear evaluation has been limited. Recently however, diffusion tensor (DT) MRI has been successful in detecting abnormalities in muscle tissue induced by disease or traumatic injury (Zaraiskaya, et al. 2006). DT-MRI offers not only a way to diagnose skeletal muscle disruption, but also to quantify the abnormality within the muscle tissue. As a noninvasive technique, DT-MRI makes it possible to perform repeat imaging and evaluate the duration and severity of the muscle injury over time. Strenuous unaccustomed exercise induces ultrastructural evidence of skeletal muscle disruption; however the potential use of DT-MRI to investigate exercise-induced muscle tissue alteration has not been examined. PURPOSE: To investigate acute changes in musculoskeletal structure following a high-force eccentric exercise protocol previously shown to induce skeletal muscle disruption (Beaton et al., Med Sci Sports Exerc 34:798-805, 2002). METHODS: A healthy active 39 year old man performed 24 sets of 10 maximal eccentric actions with the leg extensors of the dominant limb on a Biodex isokinetic dynamometer (0.52 rad·s-1), separated by 30 s rest. The contralateral leg served as a non-exercised control limb. Isometric peak torque and DT-MRI was measured before and after exercise. DT-MRI was performed using a GE 3T excite-HD MRI system using 6 diffusion encoding directions (4 NEX, FOV=20cm, TE/TR=67/6000, 64x64 matrix, 3mm thick, 0 skip, b=400s/mm2). DT-MRI fractional anisotropy (FA) analysis was performed using diffusion toolkit software; while fiber tracking for visualization of muscle tears was done using TrackVis software. RESULTS: Isometric peak torque was lower immediately following exercise compared to pre-exercise (202 vs 237 N·m) and remained lower after 24 h (208 N·m) of recovery. An increase in FA (typically seen in tissues that are exhibiting loss of cell membrane integrity) as well as muscle fiber disorganization was observed in the exercised limb. These changes were not visualized in the contralateral control limb. CONCLUSION: Preliminary data suggest that DT-MRI is very sensitive to muscle tissue alteration following high-force eccentric exercise.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| 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".