Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics

Jinao Zhang, Yongmin Zhong, Chengfan Gu

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. [Figure not available: see fulltext.].

Original languageEnglish
Pages (from-to)2163-2176
Number of pages14
JournalMedical and Biological Engineering and Computing
Volume56
Issue number12
DOIs
Publication statusPublished - 1 Dec 2018
Externally publishedYes

Keywords

  • Cellular neural networks
  • Haptic feedback
  • Reaction-diffusion mechanics
  • Real-time performance
  • Soft tissue deformation

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