In Vivo Determination of the Physiological and Functional Properties of Muscle Using Multi-Scale Measurements of Muscle Architecture

Terrell, Evan, Biomedical Engineering - School of Engineering and Applied Science, University of Virginia
Blemker, Silvia, Department of Biomedical Engineering, University of Virginia

Muscle architecture, defined as the spatial arrangement of fiber bundles relative to the axis of force generation, is a critical determinant of muscle functional capacity. Computational models are routinely used in the field of biomechanics to estimate muscle force generation and simulate human movement based on architecture inputs, providing an efficient and flexible platform to study human performance, explore the effects of pathologies, and develop novel therapies. Existing muscle architecture data is limited, often coming from cadaver dissection studies, which fail to accurately represent the architecture of healthy musculature. There exists a critical need for comprehensive, accurate human muscle architecture data upon which reliable musculoskeletal models can be developed and validated. Fortunately, new in vivo imaging technologies now present the opportunity to address this need.

The objectives of this work were to apply a multi-scale in vivo imaging framework to study the architecture of the tibialis anterior (TA) muscle non-invasively in healthy adult subjects. Magnetic resonance imaging was used to measure the volume of the muscle. Ultrasound imaging was applied to explore fascicular architecture and estimate tendon moment arm. Finally, a recently developed laser-based micro-endoscopic imaging system was used to measure the length of muscle sarcomeres. Using this information, we have, for the first time, calculated the optimal fascicle length and physiological cross-sectional area (PCSA) of the TA entirely from in vivo measurements of muscle architecture. Operating range and force-production capacity were then estimated, allowing us to predict dorsiflexion moments produced about the ankle joint by the TA. These predictions were compared against experimentally measured dorsiflexion moments to evaluate the accuracy of architecture-based estimates. Lastly, the sensitivity of moment estimates to architecture parameter values and methodology was explored, providing an indication of the factors most critical to the development of reliable computational models.

Architecture measurements in this study agreed well with published data for the TA muscle. Dorsiflexion torque estimates derived from measured muscle architecture were found to differ significantly from measured dorsiflexion torque (p < 0.0005). Among the architectural parameters measured in this work, moment estimates were found to be most sensitive to variations in sarcomere length. Sensitivity of our computational approach to the cumulative effects of methodological variants and measured parameter values was more than sufficient to explain discrepancies between measured and estimated dorsiflexion moments, indicating that minor inaccuracies in architecture measurements can confound predictions of muscle function in computational models.

MS (Master of Science)
Biomechanics, Muscle, Modeling, Imaging
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