Department of Psychological and Brain Sciences, University of California, Santa Barbara, United States.
Department of Computer Science, University of California, Santa Barbara, United States.
Neuroimage. 2018 Apr 1;169:473-484. doi: 10.1016/j.neuroimage.2017.12.039. Epub 2017 Dec 22.
White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion-weighted MRI (dMRI). These long-range projections are typically characterized in a two-step process: dMRI signal is used to estimate the orientation of axon segments within each voxel, then these local orientations are linked together to estimate the spatial extent of putative white matter bundles. Tractography, the process of tracing bundles across voxels, either requires computationally expensive (probabilistic) simulations to model uncertainty in fiber orientation or ignores it completely (deterministic). Furthermore, simulation necessarily generates a finite number of trajectories, introducing "simulation error" to trajectory estimates. Here we introduce a method to analytically (via a closed-form solution) take an orientation distribution function (ODF) from each voxel and calculate the probabilities that a trajectory projects from a voxel into each directly adjacent voxels. We validate our method by demonstrating experimentally that probabilistic simulations converge to our analytically computed transition probabilities at the voxel level as the number of simulated seeds increases. We then show that our method accurately calculates the ground-truth transition probabilities from a publicly available phantom dataset. As a demonstration, we incorporate our analytic method for voxel transition probabilities into the Voxel Graph framework, creating a quantitative framework for assessing white matter structure, which we call "analytic tractography". The long-range connectivity problem is reduced to finding paths in a graph whose adjacency structure reflects voxel-to-voxel analytic transition probabilities. We demonstrate that this approach performs comparably to the current most widely-used probabilistic and deterministic approaches at a fraction of the computational cost. We also demonstrate that analytic tractography works on multiple diffusion sampling schemes, reconstruction method or parameters used to define paths. Open source software compatible with popular dMRI reconstruction software is provided.
活人大脑白质结构由髓鞘化轴突组成,主要通过扩散加权 MRI(dMRI)进行研究。这些长程投射通常通过两步过程进行特征描述:dMRI 信号用于估计每个体素内轴突段的方向,然后将这些局部方向链接在一起,以估计假定白质束的空间范围。束追踪是在体素之间追踪束的过程,要么需要进行计算成本高昂的(概率性)模拟来模拟纤维方向的不确定性,要么完全忽略它(确定性)。此外,模拟必然会生成有限数量的轨迹,从而给轨迹估计带来“模拟误差”。在这里,我们介绍了一种方法,可以从每个体素中分析地(通过闭式解)获取方向分布函数(ODF),并计算轨迹从一个体素投影到每个直接相邻体素的概率。我们通过实验证明了我们的方法的有效性,即在模拟种子数量增加时,概率模拟在体素水平上收敛到我们分析计算的转移概率。然后,我们展示了我们的方法如何准确地从公共可用的幻影数据集计算出真实的转移概率。作为一个演示,我们将我们的体素转移概率分析方法整合到 Voxel Graph 框架中,创建了一个用于评估白质结构的定量框架,我们称之为“分析束追踪”。长程连接问题被简化为在图中找到路径,其邻接结构反映了体素到体素的分析转移概率。我们证明,与当前最广泛使用的概率和确定性方法相比,这种方法的计算成本要低得多。我们还证明,分析束追踪适用于多种扩散采样方案、重建方法或用于定义路径的参数。提供了与流行的 dMRI 重建软件兼容的开源软件。