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HyperMSM: A new MSM variant for efficient simulation of dynamic soft-tissue deformations.

Abbass Ballit1, Tien-Tuan Dao1

  • 1Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59655 Villeneuve d'Ascq Cedex, F-59000, Lille, France.

Computer Methods and Programs in Biomedicine
|February 2, 2022
PubMed
Summary

A new hyperelastic Mass-Spring Model (MSM) called HyperMSM accurately simulates soft tissue deformation. This efficient formulation enhances non-linear material simulation for applications like prosthetic limbs and muscles.

Keywords:
Hyperelastic materialMass-Spring ModelNeo-Hookean solidReal-time simulationSoft tissue deformation

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Area of Science:

  • Computational mechanics
  • Biomedical engineering
  • Material science

Background:

  • Simulating soft tissue deformation requires speed, accuracy, and stability.
  • Mass-Spring Models (MSM) offer fast, simple dynamic simulations but struggle with non-linear materials.
  • Accurate modeling of hyperelasticity in MSM remains a challenge.

Purpose of the Study:

  • Develop and evaluate an efficient hyperelastic Mass-Spring Model (MSM) for simulating Neo-Hookean deformable materials.
  • Introduce a novel formulation, HyperMSM, to address limitations in current MSM approaches.
  • Enhance the simulation accuracy and efficiency of soft tissue deformation modeling.

Main Methods:

  • Introduced a novel HyperMSM formulation compatible with tetrahedral and hexahedral meshes.
  • Integrated variable rest-length springs and a volume conservation constraint.
  • Validated the model using transtibial residual limb and skeletal muscle simulations.

Main Results:

  • HyperMSM achieved low Root Mean Square Errors (RMSE) compared to finite element methods: 2.8%-5.2% for stress-strain and 0.46%-5.4% for volumetric responses.
  • Demonstrated high accuracy in displacement error for a transtibial residual limb model (0.01mm-0.7mm).
  • Showcased efficient simulation of skeletal muscle deformation with low relative nodal displacement errors (0.4%-1.7%).

Conclusions:

  • The HyperMSM formulation accurately and efficiently models hyperelastic soft tissue behavior within the MSM framework.
  • Future work includes enhancing the model with electrical properties for multi-physical simulations.
  • Integration with augmented reality environments is planned for future applications.