Saurabh Jain, D. Tward, David S. Lee, Anthony Kolasny, Timothy Brown, J. Ratnanather, M. Miller, L. Younes
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引用次数: 12
Abstract
Computational Anatomy (CA) is a discipline focused on the quantitative analysis of the variability in biological shape. The Large Deformation Diffeomorphic Metric Mapping (LDDMM) is the key algorithm which assigns computable descriptors of anatomical shapes and a metric distance between shapes. This is achieved by describing populations of anatomical shapes as a group of diffeomorphic transformations applied to a template, and using a metric on the space of diffeomorphisms. LDDMM is being used extensively in the neuroimaging (www.mristudio.org) and cardiovascular imaging (www.cvrgrid.org) communities. There are two major components involved in shape analysis using this paradigm. First is the estimation of the template, and second is calculating the diffeomorphisms mapping the template to each subject in the population. Template estimation is a computationally expensive problem, which involves an iterative process, where each iteration calculates one diffeomorphism for each target. These can be calculated in parallel and independently of each other, and XSEDE is providing the resources, in particular those provided by the cluster Stampede, that make these computations for large populations possible. Mappings from the estimated template to each subject can also be run in parallel. In addition, the use of NVIDIA Tesla GPUs available on Stampede present the possibility of speeding up certain convolution-like calculations which lend themselves well to the General Purpose GPU computation model. We are also exploring the use of the available Xeon Phi Co-processors to increase the efficiency of our codes. This will have a huge impact on both the neuroimaging and cardiac imaging communities as we bring these shape analysis tools online for use by these communities through our webservice (www.mricloud.org), with the XSEDE Computational Anatomy Gateway providing the resources to handle the computational demands for large populations.
计算解剖学(CA)是一门专注于生物形状可变性定量分析的学科。大变形微分同构度量映射(LDDMM)是分配可计算的解剖形状描述符和形状之间度量距离的关键算法。这是通过将解剖形状的种群描述为应用于模板的一组微分同构变换,并使用微分同构空间上的度量来实现的。LDDMM被广泛应用于神经影像学(www.mristudio.org)和心血管影像学(www.cvrgrid.org)领域。在使用这种范式进行形状分析时,有两个主要组成部分。首先是模板的估计,其次是计算将模板映射到总体中每个受试者的微分同态。模板估计是一个计算量很大的问题,它涉及一个迭代过程,其中每次迭代计算每个目标的一个微分同构。这些计算可以并行且彼此独立地进行,XSEDE提供了资源,特别是由集群Stampede提供的资源,使这些计算成为可能。从预估模板到每个主题的映射也可以并行运行。此外,Stampede上可用的NVIDIA Tesla GPU的使用提供了加速某些类似卷积的计算的可能性,这些计算非常适合通用GPU计算模型。我们也在探索使用现有的Xeon Phi协处理器来提高我们代码的效率。这将对神经成像和心脏成像社区产生巨大的影响,因为我们将这些形状分析工具通过我们的网络服务(www.mricloud.org)提供给这些社区使用,XSEDE计算解剖网关提供资源来处理大量人口的计算需求。