基于局部空间数据的功能磁共振图像运动校正

Local motion correction for functional magnetic resonance images

在脑功能磁共振实验中,运动校正是数据预处理的关键环节。运动校正的结果对后续的脑区定位、功能连接等分析有着重要的影响。但因数据量较大,常规分析软件对实验数据进行运动校正时做了一些简化处理,校正误差较大。为减少这种误差,提出了一种基于局部空间数据的运动校正方法,首先从数据获取的角度构造功能像各切片的局部空间数据,然后利用修正的Gauss-Newton最优化方法估计各切片相对于参考图像做刚体变换后的空间位置,最后利用Delaunay三角剖分方法重构功能像以实现精确校正。仿真实验及实际的视觉实验数据分析结果表明,该方法具有较高的校正精度,是一种有效的功能磁共振数据运动校正方法。

In brain functional Magnetic Resonance Imaging （fMRI） experiment, motion correction is an important step in data preprocessing. Results of motion correction affect the follow up analysis such as detecting the functional activation area and functional connectivity. There are some simplified hypotheses for head motion in the common analysis software packages. Due to large volume of data, the correction error is also large. In order to reduce the correction error, a novel motion correction method was proposed based on local rigid transform. This method first used adjacent weighted slices to construct local volumetric data for each slice in a multi-slice echo planar imaging volume data, and then estimated the space position of each slice by the registration of local volumetric data using the modified Gauss-Newton optimization algorithm. Finally the image stack was re-liced using Delannay triangulation method. Results of implementation based on this method during phantom data and human vision experiments reveal that it is effective to reduce the correction error, which leads to accurate realignment.