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Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle.

ABSTRACT: PURPOSE:To establish a series of relationships defining how muscle microstructure and diffusion tensor imaging (DTI) are related. METHODS:The relationship among key microstructural features of skeletal muscle (fiber size, fibrosis, edema, and permeability) and the diffusion tensor were systematically simulated over physiologically relevant dimensions individually, and in combination, using a numerical simulation application. Stepwise multiple regression was used to identify which microstructural features of muscle significantly predict the diffusion tensor using single-echo and multi-echo DTI pulse sequences. Simulations were also performed in models with histology-informed geometry to investigate the relationship between fiber size and the diffusion tensor in models with real muscle geometry. RESULTS:Fiber size is the strongest predictor of ?2, ?3, mean diffusivity, and fractional anisotropy in skeletal muscle, accounting for approximately 40% of the variance in the diffusion model when calculated with single-echo DTI. This increased to approximately 70% when diffusion measures were calculated from the short T2 component of the multi-echo DTI sequence. This nonlinear relationship begins to plateau in fibers with greater than 60-?m diameter. CONCLUSIONS:As the normal fiber size of a human muscle fiber is 40 to 60??m, this suggests that DTI is a sensitive tool to monitor muscle atrophy, but may be limited in measurements of muscle with larger fibers. Magn Reson Med 80:317-329, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

SUBMITTER: Berry DB

PROVIDER: S-EPMC5876103 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle.

Berry David B DB Regner Benjamin B Galinsky Vitaly V Ward Samuel R SR Frank Lawrence R LR

Magnetic resonance in medicine 20171031 1

<h4>Purpose</h4>To establish a series of relationships defining how muscle microstructure and diffusion tensor imaging (DTI) are related.<h4>Methods</h4>The relationship among key microstructural features of skeletal muscle (fiber size, fibrosis, edema, and permeability) and the diffusion tensor were systematically simulated over physiologically relevant dimensions individually, and in combination, using a numerical simulation application. Stepwise multiple regression was used to identify which ...[more]

PMID: 29090480

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Project description:BackgroundThe purpose of this study was to develop a DTI-based method to quantitatively assess fiber angles and changes therein in leg muscles in order to facilitate longitudinal studies on muscle fiber architectural adaptations in healthy subjects.MethodsThe upper legs of five volunteers were scanned twice on the same day. The right lower legs of five volunteers were scanned twice with the ankle in three positions, i.e. -15° dorsiflexion, 0° neutral position, and 30° plantarflexion. The MRI protocols consisted of a noise scan, a 3-point mDixon scan and a DTI scan. Fiber-angle color maps were generated for four muscles in the upper legs and two muscles in the lower leg. Voxel-wise fiber angles (θ) were calculated from the angle between the principal eigenvector of the diffusion tensor and a reference line defined between the origo and insertion points of each muscle. Bland-Altman analysis, intraclass correlation coefficient (ICC), coefficient of variation (CV%), minimal detectable change (MDC), standard error (SE) and Friedman test were used for assessing the feasibility of this method and in order to have an indication of the repeatability and the sensitivity.ResultsBland-Altman analysis showed good repeatability (CV%<10 and 0.7≤ICC≤0.9) with exception of the Tibialis Anterior (TA) muscle in dorsiflexion position(CV%: 12.2) and the Semitendinosus (ST) muscle (left leg) (CV%: 11.4). The best repeatability metrics were found for the SOL muscle in neutral position (CV%: 2.6). Changes in average θ in TA and SOL with ankle positions were observed in accordance with expected agonist and antagonist functions of both muscles. For example, for the anterior left compartment the change in fiber angle Δθ with respect to the neutral position Δθ = -1.6° ± 0.8° and 2.2° ± 2.8° (p = 0.008), for dorsiflexion and plantarflexion, respectively.ConclusionOur method facilitates fast inspection and quantification of muscle fiber angles in the lower and upper leg muscles in rest and detection of changes in lower-leg muscle fiber angles with varying ankle angles.

Project description:Purpose: T2 mapping and diffusion tensor imaging (DTI) enable the detection of changes in the skeletal muscle microenvironment. We assessed T2 relaxation times, DTI metrics, performed histological characterization of frostbite-induced skeletal muscle injury and repair, and provided diagnostic imaging biomarkers. Design and Methods: Thirty-six Sprague Dawley rats (200 ± 10 g) were obtained. Thirty rats were used for establishing a skeletal muscle frostbite model, and six were untreated controls. Functional MR sequences were performed on rats on days 0, 3, 5, 10, and 14 (n = 6 per time point). Rats were then sacrificed to obtain the quadriceps muscles. Tensor eigenvalues (λ1, λ2, and λ3), mean diffusivity (MD), fractional anisotropy (FA), and T2 values were compared between the frostbite model and control rats. ImageJ was used to measure the extracellular area fraction (EAF), muscle fiber cross-sectional area (fCSA), and skeletal muscle tumor necrosis factor α (TNF-α), and Myod1 expression. The correlation between the histological and imaging parameters of the frostbitten skeletal muscle was evaluated. Kolmogorov-Smirnoff test, Leven's test, one-way ANOVA, and Spearman coefficient were used for analysis. Results: T2 relaxation time of frostbitten skeletal muscle was higher at all time points (p < 0.01). T2 relaxation time correlated with EAF, and TNF-α and Myod1 expression (r = 0.42, p < 0.05; r = 0.86, p < 0.01; r = 0.84, p < 0.01). The average tensor metrics (MD, λ1, λ2, and λ3) of skeletal muscle at 3 and 5 days of frostbite increased (p < 0.05), and fCSA correlated with λ1, λ2, and λ3, and MD (r = 0.65, p < 0.01; r = 0.48, p < 0.01; r = 0.52, p < 0.01; r = 0.62, p < 0.01). Conclusion: T2 mapping and DTI imaging detect frostbite-induced skeletal muscle injury early. This combined approach can quantitatively assess skeletal muscle repair and regeneration within 2 weeks of frostbite. Imaging biomarkers for the diagnosis of frostbite were suggested.

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Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle.

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Relationships between tissue microstructure and the diffusion tensor in simulated skeletal muscle.

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