We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.

我们提出了一种使用稀疏最近点变换（SCPT）处理扩散 MRI 纤维束成像数据集的新方法。纤维束成像技术能够重建白质通路的 3D 几何结构；然而，处理它们的算法通常是高度定制的，因此没有利用现有丰富的机器学习 (ML) 算法。我们研究了一种向量空间纤维束成像表示，旨在通过使用 SCPT 来弥补这一差距，该表示由两个步骤组成：首先，从纤维束成像数据集中提取稀疏且有代表性的地标，第二步，使用最近点变换相对于这些地标进行变换曲线。我们探索了它在三个典型任务中的用途：纤维束聚类、简化和群体选择。该聚类算法使用非参数 k 均值聚类算法对来自单个全脑数据集的纤维进行分组，其性能与三种替代方法和跨四个数据集的性能进行了比较。简化算法删除了冗余曲线以改进交互式可视化，并相对于随机子采样来衡量性能。该选择算法使用源自图集原型的一类高斯分类器在总体中提取束，与基于掩模的手动选择相比，其性能通过扫描-重新扫描可靠性和对正常老化的敏感性来衡量。我们的结果展示了 SCPT 如何实现现有向量空间 ML 算法的新颖应用，从而为纤维束成像处理创建有效且高效的工具。我们的实验数据可在线获取，我们的软件实现可在定量成像工具包中获取。