Zhao Yu, Zhang Shu, Chen Hanbo, Zhang Wei, Jinglei Lv, Jiang Xi, Shen Dinggang, Liu Tianming
Department of Computer Science, University of Georgia, Athens, GA.
Queensland Institute of Medical Research (QIMR) Berghofer, Herston, QLD, Australia.
Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:485-489. doi: 10.1109/ISBI.2017.7950566. Epub 2017 Jun 19.
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.
精确配准在群体功能磁共振成像（fMRI）图像分析中起着关键作用，因为不同脑图像之间的空间对应是推断有意义模式的前提条件。然而，这个问题具有挑战性且尚未解决，需要付出更多努力来推进fMRI图像的先进图像配准方法。受不同个体间可从fMRI图像重建共同功能网络这一观察结果的启发，我们提出了一种新颖的计算框架，通过利用这些共同功能网络作为空间对齐的参考，来同时进行群体fMRI图像配准。在此框架中，首先，使用独立成分分析（ICA）推断每个受试者的个体化功能网络；其次，应用以所有受试者堆叠独立成分（IC）的熵为目标函数的凝聚式群体配准，来配准个体功能图谱以实现最大匹配。所提出的框架通过一个阿尔茨海默病（AD）fMRI数据集进行了评估并应用，结果显示相当不错。
