Diffusion‐tensor magnetic resonance imaging (DT‐MRI) offers objective measures of muscle characteristics, providing insights into age‐related changes. We used DT‐MRI to probe skeletal muscle microstructure and architecture in a large healthy‐aging cohort, with the aim of characterizing age‐related differences and comparing these to muscle strength. We recruited 94 participants (43 female; median age = 56, range = 22–89 years) and measured microstructure parameters—fractional anisotropy (FA) and mean diffusivity (MD)—in 12 thigh muscles, and architecture parameters—pennation angle, fascicle length, fiber curvature, and physiological cross‐sectional area (PCSA)—in the rectus femoris (RF) and biceps femoris longus (BFL). Knee extension and flexion torques were also measured for comparison to architecture measures. FA and MD were associated with age (β = 0.33, p = 0.001, R² = 0.10; and β = −0.36, p < 0.001, R² = 0.12), and FA was negatively associated with Type I fiber proportions from the literature (β = −0.70, p = 0.024, and R² = 0.43). Pennation angle, fiber curvature, fascicle length, and PCSA were associated with age in the RF (β = −0.22, 0.26, −0.23, and −0.31, respectively; p < 0.05), while in the BFL only curvature and fascicle length were associated with age (β = 0.36, and −0.40, respectively; p < 0.001). In the RF, pennation angle and PCSA were associated with strength (β = 0.29, and 0.46, respectively; p < 0.01); in the BFL, only PCSA was associated with strength (β = 0.43; p < 0.001). Our results show skeletal muscle architectural changes with aging and intermuscular differences in the microstructure. DT‐MRI may prove useful for elucidating muscle changes in the early stages of sarcopenia and monitoring interventions aimed at preventing age‐associated microstructural changes in muscle that lead to functional impairment.

The objective of this pilot study was to characterize relationships between skeletal muscle energy metabolism and body composition in healthy adults with varied amounts and distribution of adipose tissue. In vivo muscle energetics were quantified using dynamic ³¹P magnetic resonance spectroscopy with knee extension exercise standardized to subject lean body mass. Spearman’s correlation analysis examined relationships between muscle metabolism indices and measures of adiposity including body mass index (BMI), total body fat, and quadriceps intermuscular adipose tissue (IMAT). Post hoc partial correlations were examined controlling for additional body composition measures. Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons evaluated group differences in energy metabolism based on body composition profiles (i.e., lean, normal-weight obese, and overweight-obese) and IMAT tertiles. BMI negatively correlated with end-exercise muscle pH after correcting for IMAT and total body fat (r = −0.46, P = 0.034). Total adiposity negatively correlated with maximum oxidative capacity after correcting for IMAT (r = −0.54, P = 0.013). IMAT positively correlated with muscle proton buffering capacity after correcting for total body fat (r = 0.53, P = 0.023). Body composition groups showed differences in end-exercise fall in [PCr] with normalized workload (P = 0.036; post hoc: overweight-obese < lean, P = 0.029) and maximum oxidative capacity (P = 0.021; post hoc: normal-weight obese < lean, P = 0.016). IMAT tertiles showed differences in end-exercise fall in [PCr] with normalized workload (P = 0.035; post hoc: 3rd < 1st, P = 0.047). Taken together, these results support increased adiposity is associated with reduced muscle energetic efficiency with more reliance on glycolysis, and when accompanied with reduced lean mass, is associated with reduced maximum oxidative capacity. NEW & NOTEWORTHY Skeletal muscle energy production is influenced by both lean body mass and adipose tissue but the effect of their distribution on energy metabolism is unclear. This study examined variations in quadriceps muscle energy metabolism in healthy adults with varied relative amounts of lean and adipose tissue. Results suggest increased adiposity is associated with reduced muscle energetic efficiency with more reliance on glycolysis, and when accompanied with reduced lean mass, is associated with reduced maximum oxidative capacity.

Increased sensitivity in the characterization of cartilage matrix status by magnetic resonance (MR) imaging, through the identification of surrogate markers for tissue quality, would be of great use in the noninvasive evaluation of engineered cartilage. Recent advances in MR evaluation of cartilage include multiexponential and multiparametric analysis, which we now extend to engineered cartilage. We studied constructs which developed from chondrocytes seeded in collagen hydrogels. MR measurements of transverse relaxation times were performed on samples after 1, 2, 3, and 4 weeks of development. Corresponding biochemical measurements of sulfated glycosaminoglycan (sGAG) were also performed. sGAG per wet weight increased from 7.74±1.34 μg/mg in week 1 to 21.06±4.14 μg/mg in week 4. Using multiexponential T₂ analysis, we detected at least three distinct water compartments, with T₂ values and weight fractions of (45 ms, 3%), (200 ms, 4%), and (500 ms, 97%), respectively. These values are consistent with known properties of engineered cartilage and previous studies of native cartilage. Correlations between sGAG and MR measurements were examined using conventional univariate analysis with T₂ data from monoexponential fits with individual multiexponential compartment fractions and sums of these fractions, through multiple linear regression based on linear combinations of fractions, and, finally, with multivariate analysis using the support vector regression (SVR) formalism. The phenomenological relationship between T₂ from monoexponential fitting and sGAG exhibited a correlation coefficient of r²=0.56, comparable to the more physically motivated correlations between individual fractions or sums of fractions and sGAG; the correlation based on the sum of the two proteoglycan-associated fractions was r²=0.58. Correlations between measured sGAG and those calculated using standard linear regression were more modest, with r² in the range 0.43–0.54. However, correlations using SVR exhibited r² values in the range 0.68–0.93. These results indicate that the SVR-based multivariate approach was able to determine tissue sGAG with substantially higher accuracy than conventional monoexponential T₂ measurements or conventional regression modeling based on water fractions. This combined technique, in which the results of multiexponential analysis are examined with multivariate statistical techniques, holds the potential to greatly improve the accuracy of cartilage matrix characterization in engineered constructs using noninvasive MR data.

Intravoxel incoherent motion (IVIM) MRI allows for simultaneous assessment of tissue microcirculation (perfusion) and diffusion of water. In single‐center studies, IVIM has shown great potential for diagnosis, treatment outcome prediction, and treatment monitoring for many different diseases and organs. However, heterogeneity in data acquisition protocols, pre‐processing pipelines, and post‐processing routines yields differences in reported IVIM parameters, which has constrained large‐scale deployment of IVIM. Moreover, deploying IVIM protocols and analysis typically requires technical expertise, further challenging wider use, especially for clinicians. In this consensus paper, to accelerate the deployment of IVIM, we provide recommendations and harmonize protocols for brain, breast, kidney, liver, muscle, and pancreas IVIM studies. For this goal we organized multiple questionnaires and held a dedicated workshop. To ensure a level of standardized, reproducible results, without restricting innovation, we suggest a small subset of b ‐values to always be measured and analyzed separately, and to which more extensive b ‐value sampling can be added for advanced investigations. We further introduce detailed recommendations on acquisition protocols and analysis pipelines. To increase consistency, repeatability, and reproducibility, we highly recommend that these protocols and pipelines be deployed by scientists and clinicians for IVIM studies. For advanced users who desire different protocols or analysis approaches, we suggest adding results from our suggested protocols and analysis pipeline in the supplemental part of their paper to enable retrospective studies.