NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.
NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics.
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DOI：
10.1007/s11548-014-1118-5
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发表时间：
2015-07
影响因子：
3
通讯作者：
Ourselin, Sebastien
中科院分区：
文献类型：
--
作者：
Johnsen, Stian F.;Taylor, Zeike A.;Clarkson, Matthew J.;Hipwell, John;Modat, Marc;Eiben, Bjoern;Han, Lianghao;Hu, Yipeng;Mertzanidou, Thomy;Hawkes, David J.;Ourselin, Sebastien
NiftySim是一个开源有限元工具包，旨在将高性能软组织模拟功能纳入生物医学应用。该工具包在一个简单易用的库中提供了在快速图形处理单元（GPU）硬件上执行的选项、众多的本构模型和实体单元选项、膜和壳单元以及接触建模设施。该工具包是建立在总拉格朗日显式动力学（TLEDs）算法，这已被证明是有效和准确的模拟软组织。基本代码是用C编写的，GPU执行是使用nVidia CUDA框架实现的。在大多数情况下，与底层求解器的交互可以通过单个Simulator类来实现，该类可以直接嵌入到第三方应用程序中，例如手术导航系统。还提供了接触建模和非线性本构模型等先进功能，以及降阶建模等更多实验技术。底层的解决方案算法，其实施与GPU执行的重点一致的描述，并提供了该工具包在生物医学应用中的使用的例子。TLED算法到并行硬件的高效映射导致非常高的计算性能，远远超过商业软件包中可用的计算性能。NiftySim工具包使用GPU技术为医学图像计算、手术模拟和手术指导应用中的生物力学模拟研究应用提供高性能软组织模拟功能。
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. 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, 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. Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages. 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.
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