A Novel Framework for Groupwise Registration of fMRI Images based on Common Functional Networks.
A Novel Framework for Groupwise Registration of fMRI Images based on Common Functional Networks.
复制标题
DOI：
10.1109/isbi.2017.7950566
复制
发表时间：
2017-04
期刊：
影响因子：
--
通讯作者：
Liu T
中科院分区：
文献类型：
--
作者：
Zhao Y;Zhang S;Chen H;Zhang W;Jinglei L;Jiang X;Shen D;Liu T
准确的配准在功能性磁共振成像（fMRI）图像分析中起着至关重要的作用，因为不同脑图像之间的空间对应性是推断有意义模式的先决条件。然而，这个问题是具有挑战性的，仍然是开放的，应该作出更多的努力，以推进国家的最先进的图像配准方法的功能磁共振图像。受此启发，我们提出了一种新的计算框架，同时groupwise fMRI图像配准利用这些常见的功能网络作为空间对齐的参考，可以从fMRI图像跨个人重建的共同功能网络。在该框架中，首先利用独立成分分析（伊卡）推断出每个受试者的个体化功能网络;其次，将所有受试者的堆叠独立成分（IC）的熵作为目标函数，采用凝块分组配准方法配准个体功能图，实现最大匹配。建议的框架进行评估，并应用于阿尔茨海默病（AD）的功能磁共振成像数据集，并显示出相当不错的结果。
Accurate registration plays a critical role in group-wise functional Magnetic Resonance Imaging (fMRI) image analysis, as spatial correspondence among different brain images is a prerequisite for inferring meaningful patterns. However, the problem is challenging and remains open, and more effort should be made to advance the state-of-the-art image registration methods for fMRI images. Inspired by the observation that common functional networks can be reconstructed from fMRI image across individuals, we propose a novel computational framework for simultaneous groupwise fMRI image registration by utilizing those common functional networks as references for spatial alignments. In this framework, firstly, individualized functional networks in each subject are inferred using Independent Component Analysis (ICA); secondly, congealing groupwise registration that takes entropy of stacked independent components (ICs) from all the subjects as objective function is applied to register individual functional maps for maximal matching. The proposed framework is evaluated by and applied to an Alzheimer’s Disease (AD) fMRI dataset and shows reasonably good results.