Vorburger Robert S, Reischauer Carolin, Boesiger Peter
Institute for Biomedical Engineering, University and ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.
Institute for Biomedical Engineering, University and ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.
Neuroimage. 2013 Feb 1;66:426-35. doi: 10.1016/j.neuroimage.2012.10.058. Epub 2012 Oct 27.
Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution. Moreover, the BootGraph can be applied to common DTI data sets without further modifications and shows a high repeatability. Thus, it is very well suited for longitudinal studies and meta-studies based on DTI.
自引导方法最近已被引入到扩散加权磁共振成像中，用于直接从采集的数据估计后续扩散参数的测量不确定性，而无需假设噪声模型。这些方法先前已与确定性流线追踪算法相结合，以评估人类大脑中的连接概率。因此，由于流线追踪算法的逐步推进，扩散数据中由局部噪声引起的干扰会累加起来。已提出基于图的方法来克服流线技术的这一缺点。因此，在本工作中，自引导方法被纳入到一个图设置中，以推导一种新的概率纤维追踪方法，称为BootGraph。通过在每个体素中定义一个顶点以及相邻顶点之间的边，将采集的数据集转换为一个加权无向图。此后，借助使用野生自引导推导的不确定性锥，为每条边分配一个权重。随后应用两种路径查找算法来推导连接概率。第一种算法基于最短路径方法，而第二种算法考虑两个顶点之间的所有现有路径。将追踪结果与基于自引导方法结合流线纤维追踪的既定算法以及另一种基于图的算法进行比较。BootGraph在交叉情况下对于假阴性表现出非常好的性能，并允许纳入额外的约束，例如曲率阈值。通过继承自引导方法和图论的优点，BootGraph方法提供了一种计算高效且灵活的概率追踪设置，用于计算连接概率图和虚拟纤维路径，而没有流线追踪算法的缺点或噪声分布的假设。此外，BootGraph可以应用于常见的DTI数据集而无需进一步修改，并且显示出高重复性。因此，它非常适合基于DTI的纵向研究和荟萃研究。
