Taylor Z A, Comas O, Cheng M, Passenger J, Hawkes D J, Atkinson D, Ourselin S
Centre for Medical Image Computing, University College London, Gower St, London, WC1E 6BT, UK.
Med Image Anal. 2009 Apr;13(2):234-44. doi: 10.1016/j.media.2008.10.001. Epub 2008 Oct 17.
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.
在医学图像计算的许多领域,如生物力学驱动的图像配准和交互式手术模拟中,高效且准确的软组织变形模拟技术正成为越来越有价值的工具。出于效率考虑,大多数分析基于简化的线性公式,并且此前几乎所有分析都忽略了组织力学响应中已确立的特征,如各向异性和时间依赖性。我们通过首先提出一种用于软组织的广义各向异性粘弹性本构框架来解决这些问题,该框架的特定情况此前已被用于对多种组织进行建模。然后,我们为伴随的粘弹性遗传积分开发了一种高效的求解方法,使得此类模型能够在显式动态有限元算法中使用。我们表明,与通常的各向同性弹性模型相比,该方法纳入各向异性和粘弹性的额外成本低至5.1%。最后,我们描述了一种使用CUDA API的基于GPU的软组织模拟新有限元方案的实现。即使包含了所述更精细的本构模型,新实现与我们最近基于图形API的实现相比仍实现了速度提升,与CPU执行相比实现了56.3倍的加速。通过一系列数值示例证明了粘弹性求解方法的有效性和GPU实现的性能。