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Fatigue Effects on the Lower Leg Muscle Architecture Using Diffusion Tensor MRI.
- Source :
- Applied Sciences (2076-3417); Oct2022, Vol. 12 Issue 19, p9767, 13p
- Publication Year :
- 2022
-
Abstract
- Proton density (PD) and diffusion tensor imaging (DTI) are imaging techniques that enable the acquisition of data from living subjects that can be used in the fine-tuning of subject-specific models' architectural parameters. The aim of this study was to determine the in vivo 3D architectural parameters (volume, pennation angle, fiber length and physiological cross-sectional area) of the gastrocnemius medialis, gastrocnemius lateralis, soleus and tibialis anterior muscles using proton density and diffusion tensor imaging data before and after an exhaustive one-legged jump exercise. These methods were used in the in vivo 3D data acquisition of six young and physically active female subjects' lower legs, followed by a fiber-tracking algorithm and analysis tools. No significant differences were found in the muscles' architecture after the exercise, with the following exceptions: the anatomical cross-section area of the gastrocnemius medialis increased (p-value 0.001, effect size 0.18) after exercise; the fiber lengths of the gastrocnemius medialis, lateralis and soleus muscles were higher after exercise (p-value 0.002, 0.001 and 0.001, respectively, and effect size 2.03, 1.29 and 0.85, respectively); and the soleus mean pennation angle decreased after exercise (p-value 0.0015, effect size 2.31). These changes (or lack thereof) could be attributed to the extended acquisition time of the MRI scans to minimize noise: by increasing the acquisition time, the effect of the exercise may have been partially lost due to muscle recovery. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 12
- Issue :
- 19
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 159675776
- Full Text :
- https://doi.org/10.3390/app12199767