基于贝叶斯约束统计框架的DT-MRI脑白质纤维追踪成像

A Bayesian Constraint Stochastic Framework for DT-MRI White Matter Fiber Tractography

弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像利用脑白质水分子弥散构成的弥散张量信息追踪脑白质纤维束并无创重建其3维结构图像。针对现有追踪方法一般以局部体素的弥散张量为主要追踪依据,缺乏对纤维结构、弥散度等人体解剖结构和生理机能的综合考量的缺陷,该文基于贝叶斯理论框架综合分析追踪路径与各体素弥散张量方向和纤维束几何结构相关性,并使用弥散度和追踪纤维角度对两者进行约束,获得各步追踪方向的概率密度分布,通过Markov Chain Monte Carlo采样确定其追踪方向进行追踪成像,通过多次追踪获得具有统计意义的3维结果。最后利用文中方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,该方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。

Diffusion Tensor Magnetic Resonance Imaging （DT-MRI） can track the brain white matter fiber by tracing the local tensor orientation and reconstruct the three dimensional image noninvasively.The commonly used tracking method is usually based on the local diffusion information and insufficient to consider the geometrical structure and fractional anisotropy which is constrained by anatomical structure and physiological function of human been.Therefore,a novel method of fiber tracking based on Bayesian constrained stochastic framework is proposed.In this method,the correlation of tracking direction to both the diffusion directions of the current voxel and the structure information of the current fiber segment is considered synthetically.Meanwhile,the two components are constrained by the fractional anisotropy and angle of the fiber curve respectively.The probability distributions of the tracking directions of the next voxel is estimated under the Bayesian constrained stochastic framework.Then,according to the probability distributions,the fiber bundle is sampled with Markov Chain Monte Carlo method and the 3D image of its structure is reconstructed under multiply tracking.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 reproducibly compared with the common methods.