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

Zhang, J, Zhong, Y and Gu, C 2018, 'Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics', Medical and Biological Engineering and Computing, vol. 56, no. 12, pp. 2163-2176.

Document type: Journal Article
Collection: Journal Articles

Title Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics
Author(s) Zhang, J
Zhong, Y
Gu, C
Year 2018
Journal name Medical and Biological Engineering and Computing
Volume number 56
Issue number 12
Start page 2163
End page 2176
Total pages 14
Publisher Springer
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.
Subject Automation and Control Engineering
Keyword(s) Cellular neural networks
Haptic feedback
Reaction-diffusion mechanics
Real-time performance
Soft tissue deformation
DOI - identifier 10.1007/s11517-018-1849-5
Copyright notice © International Federation for Medical and Biological Engineering 2018
ISSN 0140-0118
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
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