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Assessment of passive muscle elongation using Diffusion Tensor MRI: Correlation between fiber length and diffusion coefficients.

Valentina MazzoliJos OudemanKlaas NicolayMario MaasNico VerdonschotAndre M SprengersAart J NederveenMartijn FroelingGustav J Strijkers
Published in: NMR in biomedicine (2016)
In this study we investigated the changes in fiber length and diffusion parameters as a consequence of passive lengthening and stretching of the calf muscles. We hypothesized that changes in radial diffusivity (RD) are caused by changes in the muscle fiber cross sectional area (CSA) as a consequence of lengthening and shortening of the muscle. Diffusion Tensor MRI (DT-MRI) measurements were made twice in five healthy volunteers, with the foot in three different positions (30° plantarflexion, neutral position and 15° dorsiflexion). The muscles of the calf were manually segmented on co-registered high resolution anatomical scans, and maps of RD and axial diffusivity (AD) were reconstructed from the DT-MRI data. Fiber tractography was performed and mean fiber length was calculated for each muscle group. Significant negative correlations were found between the changes in RD and changes in fiber length in the dorsiflexed and plantarflexed positions, compared with the neutral foot position. Changes in AD did not correlate with changes in fiber length. Assuming a simple cylindrical model with constant volume for the muscle fiber, the changes in the muscle fiber CSA were calculated from the changes in fiber length. In line with our hypothesis, we observed a significant positive correlation of the CSA with the measured changes in RD. In conclusion, we showed that changes in diffusion coefficients induced by passive muscle stretching and lengthening can be explained by changes in muscle CSA, advancing the physiological interpretation of parameters derived from skeletal muscle DT-MRI.
Keyphrases
  • skeletal muscle
  • magnetic resonance imaging
  • contrast enhanced
  • cross sectional
  • computed tomography
  • type diabetes
  • machine learning
  • metabolic syndrome
  • mass spectrometry
  • tandem mass spectrometry