Cellular neural network modelling of soft tissue dynamics for surgical simulation

Zhang, J, Zhong, Y, Smith, J and Gu, C 2017, 'Cellular neural network modelling of soft tissue dynamics for surgical simulation', Technology and Health Care, vol. 25, no. S1, pp. 337-344.

Document type: Journal Article
Collection: Journal Articles

Title Cellular neural network modelling of soft tissue dynamics for surgical simulation
Author(s) Zhang, J
Zhong, Y
Smith, J
Gu, C
Year 2017
Journal name Technology and Health Care
Volume number 25
Issue number S1
Start page 337
End page 344
Total pages 8
Publisher IOS Press
Abstract Background: Currently, the mechanical dynamics of soft tissue deformation is achieved by numerical time integrations such as the explicit or implicit integration; however, the explicit integration is stable only under a small time step, whereas the implicit integration is computa-tionally expensive in spite of the accommodation of a large time step. Objective: This paper presents a cellular neural network method for stable simulation of soft tissue deformation dynamics. Method: The non-rigid motion equation is formulated as a cellular neural network with lo-cal connectivity of cells, and thus the dynamics of soft tissue deformation is transformed into the neural dynamics of the cellular neural network. Results: Results show that the proposed method can achieve good accuracy at a small time step. It still remains stable at a large time step, while maintaining the computational effi-ciency of the explicit integration. Conclusion: The proposed method can achieve stable soft tissue deformation with effi-ciency of explicit integration for surgical simulation.
Subject Biomechanical Engineering
Keyword(s) Soft tissue deformation
cellular neural network
dynamic systems
numerical time integration
DOI - identifier 10.3233/THC-171337
Copyright notice © 2017 IOS Press and The Authors. Creative Commons Non-Commercial License 4.0
ISSN 0928-7329
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