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On modelling of anisotropic viscoelasticity for soft tissue simulation: numerical solution and GPU execution.

机译:关于软组织模拟的各向异性粘弹性建模:数值解法和GPU执行。

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Efficient and accurate techniques for simulation of soft tissue deformation are an increasingly valuable tool in many areas of medical image computing, such as biomechanically-driven image registration and interactive surgical simulation. For reasons of efficiency most analyses are based on simplified linear formulations, and previously almost all have ignored well established features of tissue mechanical response such as anisotropy and time-dependence. We address these latter issues by firstly presenting a generalised anisotropic viscoelastic constitutive framework for soft tissues, particular cases of which have previously been used to model a wide range of tissues. We then develop an efficient solution procedure for the accompanying viscoelastic hereditary integrals which allows use of such models in explicit dynamic finite element algorithms. We show that the procedure allows incorporation of both anisotropy and viscoelasticity for as little as 5.1% additional cost compared with the usual isotropic elastic models. Finally we describe the implementation of a new GPU-based finite element scheme for soft tissue simulation using the CUDA API. Even with the inclusion of more elaborate constitutive models as described the new implementation affords speed improvements compared with our recent graphics API-based implementation, and compared with CPU execution a speed up of 56.3 x is achieved. The validity of the viscoelastic solution procedure and performance of the GPU implementation are demonstrated with a series of numerical examples.
机译:在许多医学图像计算领域,例如生物力学驱动的图像配准和交互式外科手术仿真中,用于仿真软组织变形的高效,准确的技术已成为越来越有价值的工具。出于效率的考虑,大多数分析都是基于简化的线性公式进行的,并且以前几乎所有分析都忽略了组织力学响应的完善特征,例如各向异性和时间依赖性。我们通过首先提出一种针对软组织的广义各向异性粘弹性本构框架来解决后一个问题,这些特殊情况以前曾被用于建模各种组织。然后,我们为伴随的粘弹性遗传积分开发了一种有效的求解程序,该程序允许在显式动态有限元算法中使用此类模型。我们表明,与常规的各向同性弹性模型相比,该程序允许以低至5.1%的附加成本同时包含各向异性和粘弹性。最后,我们描述了使用CUDA API进行软组织仿真的基于GPU的新有限元方案的实现。即使包含了如上所述的更详尽的本构模型,与我们最近的基于图形API的实现相比,新的实现也提供了速度方面的改进,并且与CPU执行相比,实现了56.3倍的提速。通过一系列数值示例证明了粘弹性求解过程的有效性和GPU实现的性能。

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