WHITE MATTER ARCHITECTURE FROM DT-MRI

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。DT-MRI子项目的目的是为DT-MRI数据开发新的过滤、分割和纤维束成像方法。具体的临床目标是利用DT-MRI和后处理技术检测和定位精神分裂症患者的脑白质异常。为了解决这些问题，我们正在开发基于描述各向异性扩散的基本PDE的模拟的白质束成像新技术，并开发用于可视化和交互探索3D DT-MRI数据的工具。除了支持这一核心的特定需求外，这些工具还将直接适用于早期发现发育中的婴儿大脑中的白质损伤，探索和描述多发性硬化症病变造成的白质破坏，以及神经外科手术期间重要白质束的可视化(例如，皮质脊髓束连接到初级和次级运动皮质)。在过去的一年里，DTI核心一直专注于开发使用随机方法的DTI踪迹成像的新方法。使用贝叶斯方法，我们开发了一种原则性的方法来描述纤维束位置的内在不确定性，方法是从单个点生成纤维束的分布，而不是通常的单一纤维束。从这项工作中，我们得出了新的连通性衡量标准。我们进一步继续了将纤维聚集成具有解剖学意义的束的工作，并在如何生成用于描述基于聚集空间的神经解剖学的高维图谱方面有了新的令人兴奋的结果。同样的技术也被证明对DTI数据的分组分析很有用，在这种分析中，聚类被用来寻找不同大脑区域之间的对应关系。另一项重要工作是推广了广泛使用的局部水扩散剖面张量模型。在过去的一年里，我们还继续发展了双张量模型。双张量模型减少了复杂邻域带来的不确定性。一般而言，高阶模型更容易改善扩散各向异性的跟踪和量化。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The purpose of the DT-MRI subproject is to develop novel filtering, segmentation, and tractography methods for DT-MRI data. The specific clinical objectives are to detect and localize white matter brain abnormalities in schizophrenia using techniques in DT-MRI and post-processing. To resolve these issues, we are developing new technology for white matter tractography based on simulation of the basic PDE describing anisotropic diffusion and also developing tools for visualization and interactive exploration of 3D DT-MRI data. In addition to supporting the specific needs in this core, these tools will be directly applicable to early detection of white matter damage in the developing infant brain, to exploration and characterization of white matter disruptions caused by MS lesions, and to the visualization of important white matter tracts during neurosurgical procedures (e.g., the cortical spinal tract connecting to the primary and secondary motor cortex). During the past year the DTI core has focused on developing new methods for DTI tractography using stochastic methods. Using a Bayesian approach, we have developed a principled way to describe the inherent uncertainty in fiber tract location by generating a distribution of tracts from a single point, rather than one single tract as is customary. From this work we have derived new measures of connectivity. We have further continued our work on clustering fibers into anatomically meaningful bundles, and have new exciting results on how to generate a high-dimensional atlas for describing the neuroanatomy based on the clustering space. The same technology has also proved useful for group analysis of DTI data, where clustering is used to find correspondences between regions across different brains. Another important line of work is to extend the widely used tensor model of local water diffusion profile. During the past year, we have also continued to develop the two-tensor model. The two-tensor model reduces the uncertainty generated by complex neighborhoods. In general, higher order models are apt to improve both tracking and quantification of diffusion anisotropy.