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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

The 4-triangle patch underlying the calculations with the EBST1 shell element. The central triangle and its sampling points are highlighted in red. The blue boxes show the location of the six quadratic shape functions
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Fig4: The 4-triangle patch underlying the calculations with the EBST1 shell element. The central triangle and its sampling points are highlighted in red. The blue boxes show the location of the six quadratic shape functions

Mentions: The shell element supported by NiftySim is the rotation-free EBST1 described in [8]. Computations with this element are based on quadratic shape functions defined on patches consisting of four triangles (Fig. 4) with deformation and curvature functions being sampled at the midpoints of the edges of patches’ central triangle and subsequently averaged. With this shell element, the curvature giving rise to its bending stiffness is computed from standard nodal displacements; therefore, there is no need for modifications to the time-ODE solver algorithms employed with TLED.Fig. 4


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)

The 4-triangle patch underlying the calculations with the EBST1 shell element. The central triangle and its sampling points are highlighted in red. The blue boxes show the location of the six quadratic shape functions
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: The 4-triangle patch underlying the calculations with the EBST1 shell element. The central triangle and its sampling points are highlighted in red. The blue boxes show the location of the six quadratic shape functions
Mentions: The shell element supported by NiftySim is the rotation-free EBST1 described in [8]. Computations with this element are based on quadratic shape functions defined on patches consisting of four triangles (Fig. 4) with deformation and curvature functions being sampled at the midpoints of the edges of patches’ central triangle and subsequently averaged. With this shell element, the curvature giving rise to its bending stiffness is computed from standard nodal displacements; therefore, there is no need for modifications to the time-ODE solver algorithms employed with TLED.Fig. 4

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