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Soft Tissue Hybrid Model for Real-Time Simulations.

Mario R Moreno-GuerraOscar Martínez-RomeroLuis Manuel Palacios-PinedaDaniel Olvera-TrejoJose A Diaz-ElizondoEduardo Flores-VillalbaJorge V L da SilvaAlex Elías-ZúñigaCiro A Rodríguez
Published in: Polymers (2022)
In this article, a recent formulation for real-time simulation is developed combining the strain energy density of the Spring Mass Model (SMM) with the equivalent representation of the Strain Energy Density Function (SEDF). The resulting Equivalent Energy Spring Model (EESM) is expected to provide information in real-time about the mechanical response of soft tissue when subjected to uniaxial deformations. The proposed model represents a variation of the SMM and can be used to predict the mechanical behavior of biological tissues not only during loading but also during unloading deformation states. To assess the accuracy achieved by the EESM, experimental data was collected from liver porcine samples via uniaxial loading and unloading tensile tests. Validation of the model through numerical predictions achieved a refresh rate of 31 fps (31.49 ms of computation time for each frame), achieving a coefficient of determination R 2 from 93.23% to 99.94% when compared to experimental data. The proposed hybrid formulation to characterize soft tissue mechanical behavior is fast enough for real-time simulation and captures the soft material nonlinear virgin and stress-softened effects with high accuracy.
Keyphrases
  • soft tissue
  • multiple sclerosis
  • drug delivery
  • electronic health record
  • gene expression
  • ms ms
  • machine learning
  • artificial intelligence
  • data analysis
  • stress induced