摘要
弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像可无创重建脑白质纤维的三维结构,而现有追踪成像方法一般仅考虑局部纤维的弥散倾向,对纤维束几何结构的综合考虑不足,为此提出一种贝叶斯决策概率型的纤维追踪成像算法.该算法通过纤维束当前体素的弥散张量方向和纤维束几何结构信息,利用贝叶斯决策理论估算追踪下一体素的方向概率分布;按照概率分布对纤维束进行加权采样,重建纤维束的三维结构图像.最后利用文中算法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验.仿真和实验结果表明,该算法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强.
In brain white matter fiber tractography, diffusion tensor magnetic resonance imaging （DT-MRI） can be used to reconstruct the three dimensional structures of the white matter fibers noninvasively. The commonly used tracking method is usually based on the local diffusion information and insufficient to eonsider the geometrical structure of the whole fiber bundle. Therefore, a novel method of fiber tracking using Bayesian decision stochastic model is proposed. In this method, from the diffusion tensor directions of the current voxel and the structure information of the current fiber segment, the probability distributions of the tracking directions of the next voxel is estimated using Bayesian decision theory. Then, according to the probability distributions, the fiber bundle is sampled and the 3D image of its structure is reconstructed. By the method, imaging simulations using a synthetic diffusion tensor dataset and imaging experiments using an in vivo brain DT-MRI dataset have been done. The results of the simulations and experiments demonstrate that using the method proposed, brain white matter fiber can be reconstructed properly as expected, more reliably and repeatedly compared with the common methods.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2009年第10期1387-1393,共7页
Journal of Computer-Aided Design & Computer Graphics
基金
四川省教育厅高等学校科技创新重大培育项目(09ZA029)
关键词
弥散张量磁共振成像
脑白质纤维追踪成像
贝叶斯决策概率模型
diffusion tensor magnetic resonance imaging
brain white matter fiber tractography
Bayesian decision stochastic model
作者简介
吴锡，男，1980年生，博士研究生，讲师，主要研究方向为数字图像处理、智能计算、生物医学成像及应用．
刘子骥，男，1981年生，博士研究生，助教，主要研究方向为红外图像处理．
毕务忠，男，1979年生，硕士，讲师，主要研究方向为数字图像处理．
罗代升，男，1947年生，博士，教授，博士生导师，主要研究方向为信号与信息处理、生物医学成像与图像处理．
