Abstract：
Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.
摘要：
流线牵引成像局部跟踪从纤维取向分布（FOD）函数提取的峰值方向，缺乏关于整个纤维束趋势的全球信息。因此,它容易产生错误的轨道，而错过真正的正连接。在这项工作中,我们提出了一种新的基于束特异性束图分布（BTD）函数的束特异性束图成像（BST）方法，通过在光纤束掩模中结合全局信息，直接重建从起始区域到终止区域的光纤轨迹。提出了任何高阶流线微分方程的统一框架，以描述基于扩散矢量场定义的具有不相交流线的纤维束。在全球范围内,通过最小化能量优化模型，将纤维束成像过程简化为BTD系数的估计，并通过引入束束信息来提供解剖先验，从而在先验指导下表征BTD与扩散张量向量之间的关系。在模拟霍夫上进行了实验，Sine,圈数据,ISMRM2015Tractography挑战数据，FiberCup数据,以及来自人类连接体项目（HCP）的体内数据，用于定性和定量评估。结果表明，我们的方法可以更准确地重建复杂的纤维几何结构。BTD在局部水平上减少了误差偏差和积累，在重建远程，扭曲，和大扇面。
