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基于图形处理器的实时非线性有限元计算——在神经外科手术模拟中的应用

Real-Time Nonlinear Finite Element Computations on GPU - Application to Neurosurgical Simulation.

作者信息

Joldes Grand Roman, Wittek Adam, Miller Karol

机构信息

Intelligent Systems for Medicine Laboratory, School of Mechanical Engineering, The University of Western Australia, Perth, AUSTRALIA.

出版信息

Comput Methods Appl Mech Eng. 2010 Dec 15;199(49-52):3305-3314. doi: 10.1016/j.cma.2010.06.037.

Abstract

Application of biomechanical modeling techniques in the area of medical image analysis and surgical simulation implies two conflicting requirements: accurate results and high solution speeds. Accurate results can be obtained only by using appropriate models and solution algorithms. In our previous papers we have presented algorithms and solution methods for performing accurate nonlinear finite element analysis of brain shift (which includes mixed mesh, different non-linear material models, finite deformations and brain-skull contacts) in less than a minute on a personal computer for models having up to 50.000 degrees of freedom. In this paper we present an implementation of our algorithms on a Graphics Processing Unit (GPU) using the new NVIDIA Compute Unified Device Architecture (CUDA) which leads to more than 20 times increase in the computation speed. This makes possible the use of meshes with more elements, which better represent the geometry, are easier to generate, and provide more accurate results.

摘要

生物力学建模技术在医学图像分析和手术模拟领域的应用意味着两个相互冲突的要求

准确的结果和高求解速度。只有通过使用适当的模型和求解算法才能获得准确的结果。在我们之前的论文中,我们提出了算法和求解方法,可在个人计算机上不到一分钟的时间内,对具有多达50,000个自由度的模型进行准确的脑移位非线性有限元分析(包括混合网格、不同的非线性材料模型、有限变形和脑-颅骨接触)。在本文中,我们展示了使用新型NVIDIA计算统一设备架构(CUDA)在图形处理单元(GPU)上对我们的算法进行的实现,这使得计算速度提高了20倍以上。这使得使用具有更多单元的网格成为可能,这些网格能更好地表示几何形状,更易于生成,并能提供更准确的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/144d/3003932/9de87e1f6faf/nihms222739f1.jpg

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