An electromechanical based deformable model for soft tissue simulation

  • Authors:

  • Yongmin Zhong;Bijan Shirinzadeh;Julian Smith;Chengfan Gu

  • Affiliations:

  • Department of Mechanical Engineering, Curtin University of Technology, GPO Box U1987, Perth, WA 6845, Australia;Department of Mechanical and Aerospace Engineering, Monash University, PO Box 31, Clayton, VIC 3800, Australia;Monash Medical Centre, Monash University, 246 Clayton Road, Clayton, VIC 3168, Australia;Department of Materials Engineering, Monash University, PO Box 69M, Clayton, VIC 3800, Australia

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2009

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Abstract

Objective: Soft tissue deformation is of great importance to surgery simulation. Although a significant amount of research efforts have been dedicated to simulating the behaviours of soft tissues, modelling of soft tissue deformation is still a challenging problem. This paper presents a new deformable model for simulation of soft tissue deformation from the electromechanical viewpoint of soft tissues. Methods and material: Soft tissue deformation is formulated as a reaction-diffusion process coupled with a mechanical load. The mechanical load applied to a soft tissue to cause a deformation is incorporated into the reaction-diffusion system, and consequently distributed among mass points of the soft tissue. Reaction-diffusion of mechanical load and non-rigid mechanics of motion are combined to govern the simulation dynamics of soft tissue deformation. Results: An improved reaction-diffusion model is developed to describe the distribution of the mechanical load in soft tissues. A three-layer artificial cellular neural network is constructed to solve the reaction-diffusion model for real-time simulation of soft tissue deformation. A gradient based method is established to derive internal forces from the distribution of the mechanical load. Integration with a haptic device has also been achieved to simulate soft tissue deformation with haptic feedback. Conclusions: The proposed methodology does not only predict the typical behaviours of living tissues, but it also accepts both local and large-range deformations. It also accommodates isotropic, anisotropic and inhomogeneous deformations by simple modification of diffusion coefficients.