Cellular neural network modelling of soft tissue dynamics for surgical simulation

Jinao Zhang, Yongmin Zhong, Julian Smith, Chengfan Gu

Research output: Contribution to journalConference articleResearchpeer-review

6 Citations (Scopus)

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 computationally 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 local 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 efficiency of the explicit integration. CONCLUSION: The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.

Original languageEnglish
Pages (from-to)S337-S344
Number of pages8
JournalTechnology and Health Care
Volume25
Issue numberS1
DOIs
Publication statusPublished - 21 Jul 2017
EventInternational Conference on Biomedical Engineering and Biotechnology (iCBEB) 2016 - Hangzhou, China
Duration: 1 Aug 20164 Aug 2016
Conference number: 5th

Keywords

  • Cellular neural network
  • Dynamic systems
  • Numerical time integration
  • Soft tissue deformation

Cite this

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title = "Cellular neural network modelling of soft tissue dynamics for surgical simulation",
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 computationally 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 local 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 efficiency of the explicit integration. CONCLUSION: The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.",
keywords = "Cellular neural network, Dynamic systems, Numerical time integration, Soft tissue deformation",
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Cellular neural network modelling of soft tissue dynamics for surgical simulation. / Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan.

In: Technology and Health Care, Vol. 25, No. S1, 21.07.2017, p. S337-S344.

Research output: Contribution to journalConference articleResearchpeer-review

TY - JOUR

T1 - Cellular neural network modelling of soft tissue dynamics for surgical simulation

AU - Zhang, Jinao

AU - Zhong, Yongmin

AU - Smith, Julian

AU - Gu, Chengfan

PY - 2017/7/21

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N2 - 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 computationally 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 local 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 efficiency of the explicit integration. CONCLUSION: The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.

AB - 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 computationally 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 local 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 efficiency of the explicit integration. CONCLUSION: The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.

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KW - Dynamic systems

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