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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

Overview of zero-gravity configuration estimation algorithm from Ref. [6]
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Fig7: Overview of zero-gravity configuration estimation algorithm from Ref. [6]

Mentions: This example application by Eiben et al. [6] aims to improve the results of registration of breast magnetic resonance images (MRI) from a prone to a supine patient position. The clinical motivation is that diagnostic images used in detecting breast cancer and the planning of its surgical removal are typically acquired with the patients lying on their stomach (prone). The interventions are performed with the patients lying on their back (supine) and may be guided with intra-operative imaging. Due to the softness of breast tissue, the deformation the breast undergoes between these two configurations is too large for standard image registration algorithms to cope with. For this reason, Eiben et al. proposed to estimate an artificial zero-gravity state for the pre-operative as well as the intra-operative images, in which correspondences between the two configurations can be established more easily, and subsequently refined to provide a starting position for standard B-spline nonrigid image registration. Figure 7 shows the algorithm as a diagram.Fig. 7


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)

Overview of zero-gravity configuration estimation algorithm from Ref. [6]
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Overview of zero-gravity configuration estimation algorithm from Ref. [6]
Mentions: This example application by Eiben et al. [6] aims to improve the results of registration of breast magnetic resonance images (MRI) from a prone to a supine patient position. The clinical motivation is that diagnostic images used in detecting breast cancer and the planning of its surgical removal are typically acquired with the patients lying on their stomach (prone). The interventions are performed with the patients lying on their back (supine) and may be guided with intra-operative imaging. Due to the softness of breast tissue, the deformation the breast undergoes between these two configurations is too large for standard image registration algorithms to cope with. For this reason, Eiben et al. proposed to estimate an artificial zero-gravity state for the pre-operative as well as the intra-operative images, in which correspondences between the two configurations can be established more easily, and subsequently refined to provide a starting position for standard B-spline nonrigid image registration. Figure 7 shows the algorithm as a diagram.Fig. 7

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