Computational fluid dynamics simulations for 3D muscle fiber architecture in finite element analysis: Comparisons...

Area of Science:

• Biomechanics
• Medical Imaging
• Computational Modeling

Background:

• Accurate skeletal muscle fiber orientation is crucial for biomechanical modeling of muscle tissue properties and behavior.
• Diffusion tensor imaging (DTI) provides in vivo muscle fiber architecture but is often impractical due to cost and time constraints.
• Computational fluid dynamics (CFD) tractography offers a simpler alternative for determining muscle fiber directions for finite element models.

Purpose of the Study:

• To investigate which CFD flow simulations and boundary conditions best replicate DTI-derived muscle fiber architectures.
• To compare the mechanical simulation results using CFD-derived versus DTI-derived fiber directions.
• To introduce and validate a novel boundary condition method for CFD tractography in skeletal muscles.

Main Methods:

• Implemented CFD tractography on the gastrocnemius muscle using a novel inflow direction boundary condition.
• Compared incompressible potential flow and nondimensionalized incompressible Stokes flow simulations against DTI results.
• Performed mechanical finite element simulations using muscle models with fiber directions derived from both CFD and DTI.

Main Results:

• The novel CFD boundary condition method generated muscle fiber directions comparable to DTI.
• Strain distributions in mechanical simulations were similar between Stokes flow CFD and DTI fiber models.
• CFD tractography, particularly with the novel boundary condition, provides a viable alternative to DTI for muscle modeling.

Conclusions:

• The developed CFD tractography method with specified inflow boundary conditions yields physiologically reasonable skeletal muscle fiber directions.
• Finite element simulations based on this CFD method show comparable results to those using DTI-derived fiber orientations.
• CFD tractography is a valuable tool for determining muscle fiber architecture for modeling when DTI is not feasible.