GPU-based parallel group ICA for functional magnetic resonance data
GPU-based parallel group ICA for functional magnetic resonance data
复制标题
基于 GPU 的功能磁共振数据并行组 ICA
DOI：
10.1016/j.cmpb.2015.02.002
复制
发表时间：
2015-04-01
影响因子：
6.1
通讯作者：
Xu, Qi
中科院分区：
文献类型：
--
作者：
Jing, Yanshan;Zeng, Weiming;Xu, Qi
我们的研究目标是开发一个快速并行实现的组独立成分分析（伊卡）的功能磁共振成像（fMRI）数据使用图形处理单元（GPU）。伊卡已经成为功能磁共振成像数据中识别脑功能连通性的标准方法，但其计算量大，尤其是对群体数据的分析成本巨大。GPU具有更高的并行计算能力和更低的成本，用于通用计算，这将有助于功能磁共振成像数据分析的显着。在这项研究中，并行组伊卡（PGICA）的GPU上，主要包括基于GPU的PCA使用SVD和Infomax-ICA，提出。与串行组伊卡相比，该方法在我们的实验中表现出6-11倍的显着加速和相当的精度的功能网络。该方法有望实现fMRI数据分析的实时后处理。(C)2015爱思唯尔爱尔兰有限公司版权所有。
The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6-11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis. (C) 2015 Elsevier Ireland Ltd. All rights reserved.