Physiological Cross-Sectional Surfaces: A Method for Estimating Muscle Functional Capacity from 3D Digital Models of...

Area of Science:

• Biomechanics
• Muscle Physiology
• Medical Imaging

Background:

• Physiological cross-sectional area (PCSA) is crucial for understanding muscle function.
• Conventional PCSA, functional cross-sectional area (FCSA), and geometric cross-sectional area (GCSA) are based on simplified 2D muscle models.
• These 2D models may not accurately represent complex 3D muscle architectures.

Purpose of the Study:

• To evaluate the accuracy of conventional PCSA, FCSA, and GCSA in realistic 3D muscle geometries.
• To introduce and validate a novel measure, physiological cross-sectional surface (PCSS), for estimating muscle fiber cross-sectional area in 3D.
• To compare PCSS with conventional measures across diverse muscle architectures.

Main Methods:

• Reconstruction of realistic 3D muscle architectures from medical imaging (MRI).
• Estimation of fiber orientations using Laplacian fiber reconstruction.
• Calculation and comparison of PCSS, PCSA, FCSA, and GCSA across six muscles from five architectural classes.

Main Results:

• Conventional measures (PCSA, FCSA, GCSA) did not consistently represent the summed cross-sectional area of parallel muscle fibers in 3D.
• PCSS differed significantly from conventional measures, with variations dependent on muscle architecture.
• GCSA underestimated PCSS in pennate muscles and overestimated it in others; PCSS showed high geometric fidelity and robustness.

Conclusions:

• Traditional methods for calculating muscle cross-sectional area are limited by their 2D assumptions.
• PCSS provides a geometrically rigorous and more accurate alternative for quantifying muscle fiber area in 3D.
• PCSS has the potential to improve estimations of functional muscle capacity using subject-specific imaging data.