Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.
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DOI：
10.3389/fninf.2013.00050
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发表时间：
2013
影响因子：
3.5
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Shamonin DP;Bron EE;Lelieveldt BP;Smits M;Klein S;Staring M;Alzheimer's Disease Neuroimaging Initiative
非刚性图像配准是医学图像分析中一项重要但耗时的任务。在典型的神经成像研究中，执行多个图像配准，即，用于基于图谱的分割或模板构造。因此，更快的图像配准例程将是有益的。在本文中，我们探索了通过以下几种技术的组合来加速图像配准包elastix：（i）在CPU上的并行化，以加速成本函数导数计算;（ii）在GPU上的并行化，该GPU基于ITKv 4的OpenCL框架并对其进行了扩展，以加速高斯金字塔计算和图像重建步骤;（iii）利用B样条变换模型的某些性质;（iv）进一步的软件优化。加速注册工具中采用的阿尔茨海默氏病和认知正常对照的诊断分类的T1加权MRI的基础上的研究。我们从公开的阿尔茨海默病神经影像学倡议数据库中选择了299名参与者。分类进行支持向量机的基础上，作为萎缩的标志物的灰质体积。我们评估了两种类型的战略（体素明智的和区域明智的），严重依赖于非刚性图像配准。并行化和优化在8核机器上实现了4- 5倍的加速因子。使用OpenCL，对于较大的图像，高斯金字塔的计算实现了2的加速因子，而对于重建步骤实现了15-60的加速因子。体素和区域分类方法分别具有88%和90%的受试者操作特征曲线下的面积，无论是标准配准还是加速配准。我们的结论是，图像配准包elastix大大加速，结果与非优化版本几乎相同。新功能将在下一个版本的elastix中以BSD许可证下的开源形式提供。
Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.
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通讯作者：
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