基于相邻体素选择的脑白质纤维交叉分叉问题解决方法

为了解决脑白质纤维交叉分叉问题,在传统算法的启发下,提出一种基于相邻体素选择的盘状张量分解算法。首先,选择合适的起点进行非分叉纤维的追踪,建立拟合函数数据集,得到拟合函数；其次,在纤维追踪出现交叉分叉问题时,建立该交叉分叉点及周围区域体素所对应的棋盘图；然后,计算以交叉点为中心的相邻张量的夹角,结合夹角的大小并利用得到的拟合函数进行纤维整体走行方向的估计,实现盘状张量的分解。算法既保证了局部信息的合理适用,又考虑了整体信息的影响,能够更加精确完整地跟踪纤维路径,解决纤维交叉分叉问题。与传统方法相比,本文算法可以更有效解决纤维分叉及交叉处的跟踪问题,从而使得到的纤维路径更加真实。

The method of basing on neighboring voxel selection for the white matter fiber crossing and bifurcating

In order to solve the problem of white matter fiber crossing and bifurcating, Inspired by the traditional algorithm,The solving method that based on a neighboring voxel selection for the decomposition of disk tensor is mentioned.Fistly,the algorithm choose the right point to fiber track and to establish data set then to obtain fitting function.Secondly,when the problem of white matter crossing and fiber bifurcating appears,we will draw the checkerboard chart for neighboring voxels of the point, Calculate of the angle between the point and other neighboring voxels.then be in combination with the angle and use fitting function according to the data set of fitting function to estimate global fiber direction,thus finish the decomposition of the disk tensor.The method not only uses the local information reasonablely but also consider the global information effecting. And it can finish the fiber tracking more accurately and completely. Compared with the traditional algorithm, the method is able to solve white matter crossing and fiber bifurcating more effectively,so the fiber tracking is closer to the truth.