Soft Tissue Hybrid Model for Real-Time Simulations.

Area of Science:

• Biomechanics
• Computational modeling
• Materials science

Background:

• Accurate real-time simulation of soft tissue mechanics is crucial for various applications.
• Existing models may lack the precision or speed required for dynamic analysis.
• Understanding the mechanical response of biological tissues under deformation is essential.

Purpose of the Study:

• To develop a novel computational model for real-time simulation of soft tissue mechanical behavior.
• To integrate the strain energy density of the Spring Mass Model (SMM) with an equivalent representation of the Strain Energy Density Function (SEDF).
• To predict mechanical responses during both loading and unloading deformation states.

Main Methods:

• Formulation of the Equivalent Energy Spring Model (EESM) by combining SMM and SEDF.
• Collection of experimental data from porcine liver samples using uniaxial tensile tests.
• Validation of the EESM against experimental data through numerical predictions.

Main Results:

• The EESM achieved a high refresh rate of 31 frames per second (31.49 ms computation time per frame).
• The model demonstrated high accuracy, with a coefficient of determination (R²) ranging from 93.23% to 99.94% compared to experimental data.
• The EESM accurately captured nonlinear virgin and stress-softened effects in soft tissues.

Conclusions:

• The developed Equivalent Energy Spring Model (EESM) offers a computationally efficient and accurate method for real-time simulation of soft tissue mechanics.
• The hybrid formulation successfully characterizes the complex mechanical behavior of biological tissues.
• The EESM's performance suggests its potential for applications requiring dynamic and precise mechanical response prediction.