基于高阶流线微分方程的束特异性纤维束成像分布估计
Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
AI总结：
针对传统峰值纤维束成像缺乏全局信息、易产生误差的问题，提出基于高阶流线微分方程的束特异性纤维束成像分布方法，以簇对簇方式重建纤维束，在多类数据集上验证了其重建复杂全局纤维束的优异性能。
摘要：
纤维束成像追踪从纤维取向分布（FOD）中提取的峰值方向，该方法存在扩散方向与纤维几何结构之间空间对应关系模糊的问题，容易产生错误追踪结果，同时遗漏真实的阳性连接。基于峰值的纤维束成像方法以“单对单”的方式“局部”重建流线，因此缺乏关于整个纤维束走向的全局信息。在本研究中，我们提出了一种新型纤维束成像方法，该方法基于束特异性纤维束成像分布函数，采用高阶流线微分方程，以“簇对簇”的方式重建流线束。我们提出了一个适用于任意高阶流线微分方程的统一框架，用于描述基于扩散张量矢量场定义的、具有不相交流线的纤维束。在全局层面，纤维束成像过程被简化为通过最小化能量优化模型来估计束特异性纤维束成像分布（BTD）系数的过程，并通过引入纤维束成像束信息提供解剖学先验，在先验指导下表征BTD与扩散张量矢量之间的关系。我们在模拟的Hough、Sine、Circle数据、ISMRM 2015纤维束成像挑战赛数据、FiberCup数据以及来自人类连接组计划（HCP）的在体数据上开展了实验，进行定性和定量评估。结果表明，我们的方法可以直接重建复杂的全局纤维束。BTD减少了局部层面的误差偏差和累积，在重建长程、扭曲和大扇形纤维束方面表现出更优的结果。
英文摘要：
Tractography traces the peak directions extracted from fiber orientation distribution (FOD) suffering from ambiguous spatial correspondences between diffusion directions and fiber geometry, which is prone to producing erroneous tracks while missing true positive connections. The peaks-based tractography methods 'locally' reconstructed streamlines in 'single to single' manner, thus lacking of global information about the trend of the whole fiber bundle. In this work, we propose a novel tractography method based on a bundle-specific tractogram distribution function by using a higher-order streamline differential equation, which reconstructs the streamline bundles in 'cluster to cluster' manner. 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 tensor vector field. At the global level, the tractography process is simplified as the estimation of bundle-specific tractogram distribution (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) data for qualitative and quantitative evaluation. The results demonstrate that our approach can reconstruct the complex global fiber bundles directly. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
