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NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.

Johnsen SF, Taylor ZA, Clarkson MJ, Hipwell J, Modat M, Eiben B, Han L, Hu Y, Mertzanidou T, Hawkes DJ, Ourselin S - Int J Comput Assist Radiol Surg (2014)

Bottom Line: Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling.A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided.Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.

View Article: PubMed Central - PubMed

Affiliation: Centre for Medical Image Computing, University College London, London, UK, rmapsfj@live.ucl.ac.uk.

ABSTRACT

Purpose: NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.

Methods: The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C[Formula: see text], and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided.

Results: Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.

Conclusion: The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

No MeSH data available.


Related in: MedlinePlus

Execution of the simulation defined in Fig. 1 via NiftySim ’s stand-alone executable. Left Input geometry with constraints. Right Visual output of final configuration via NiftySim ’s in-built visualisation facilities. Centre Corresponding annotated command line
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Fig2: Execution of the simulation defined in Fig. 1 via NiftySim ’s stand-alone executable. Left Input geometry with constraints. Right Visual output of final configuration via NiftySim ’s in-built visualisation facilities. Centre Corresponding annotated command line

Mentions: Figure 2 contains the first example showing the usage of NiftySim ’s stand-alone executable. It also contains an illustration of the constraints of the example model of Fig. 1.Fig. 2


NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.

Johnsen SF, Taylor ZA, Clarkson MJ, Hipwell J, Modat M, Eiben B, Han L, Hu Y, Mertzanidou T, Hawkes DJ, Ourselin S - Int J Comput Assist Radiol Surg (2014)

Execution of the simulation defined in Fig. 1 via NiftySim ’s stand-alone executable. Left Input geometry with constraints. Right Visual output of final configuration via NiftySim ’s in-built visualisation facilities. Centre Corresponding annotated command line
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4488488&req=5

Fig2: Execution of the simulation defined in Fig. 1 via NiftySim ’s stand-alone executable. Left Input geometry with constraints. Right Visual output of final configuration via NiftySim ’s in-built visualisation facilities. Centre Corresponding annotated command line
Mentions: Figure 2 contains the first example showing the usage of NiftySim ’s stand-alone executable. It also contains an illustration of the constraints of the example model of Fig. 1.Fig. 2

Bottom Line: Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling.A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided.Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.

View Article: PubMed Central - PubMed

Affiliation: Centre for Medical Image Computing, University College London, London, UK, rmapsfj@live.ucl.ac.uk.

ABSTRACT

Purpose: NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.

Methods: The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C[Formula: see text], and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit's usage in biomedical applications are provided.

Results: Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.

Conclusion: The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

No MeSH data available.


Related in: MedlinePlus