Faculty of Engineering, Universidad de Concepción, Concepción, Chile.
Centro de investigación CITIC, Universidade da Coruña, A Coruña, Spain.
Biomed Eng Online. 2020 Jun 3;19(1):42. doi: 10.1186/s12938-020-00786-z.
Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-subject variability and artifacts, the resulting clusters are difficult to process for finding common connections across subjects, specially for superficial white matter.
We present an automatic method for labeling of short association bundles on a group of subjects. The method is based on an intra-subject fiber clustering that generates compact fiber clusters. Posteriorly, the clusters are labeled based on the cortical connectivity of the fibers, taking as reference the Desikan-Killiany atlas, and named according to their relative position along one axis. Finally, two different strategies were applied and compared for the labeling of inter-subject bundles: a matching with the Hungarian algorithm, and a well-known fiber clustering algorithm, called QuickBundles.
Individual labeling was executed over four subjects, with an execution time of 3.6 min. An inspection of individual labeling based on a distance measure showed good correspondence among the four tested subjects. Two inter-subject labeling were successfully implemented and applied to 20 subjects and compared using a set of distance thresholds, ranging from a conservative value of 10 mm to a moderate value of 21 mm. Hungarian algorithm led to a high correspondence, but low reproducibility for all the thresholds, with 96 s of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 s. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 h.
We implemented a method for the automatic labeling of short bundles in individuals, based on an intra-subject clustering and the connectivity of the clusters with the cortex. The labels provide useful information for the visualization and analysis of individual connections, which is very difficult without any additional information. Furthermore, we provide two fast inter-subject bundle labeling methods. The obtained clusters could be used for performing manual or automatic connectivity analysis in individuals or across subjects.
弥散磁共振成像（diffusion MRI）是研究脑白质连接的首选非侵入性体内模态。示踪数据集包含可以分析以研究主要脑白质束的 3D 流线。纤维聚类方法已被用于自动将相似纤维分组到聚类中。然而，由于个体间的可变性和伪影，对于在个体之间寻找共同连接，特别是对于浅层白质，处理得到的聚类非常困难。
我们提出了一种基于单个体纤维聚类的方法，用于对一组对象的短连接束进行自动标记。该方法生成紧凑的纤维聚类，然后根据纤维的皮质连接对聚类进行标记，以 Desikan-Killiany 图谱为参考，并根据其沿一条轴的相对位置进行命名。最后，应用了两种不同的策略来进行体间束的标记：一种是使用匈牙利算法进行匹配，另一种是一种称为 QuickBundles 的知名纤维聚类算法。
对 4 名个体进行了个体标记，执行时间为 3.6 分钟。基于距离度量的个体标记检查表明，4 名测试个体之间具有很好的一致性。成功实现了两种体间标记，并将其应用于 20 名个体，并使用一系列距离阈值进行了比较，范围从保守的 10 毫米到适度的 21 毫米。匈牙利算法导致了高一致性，但在所有阈值下的可重复性都较低，执行时间为 96 秒。QuickBundles 导致了更好的一致性、可重复性和 9 秒的短执行时间。因此，对 20 名个体进行体间标记的整个处理过程需要 1.17 小时。
我们实现了一种基于单个体聚类和聚类与皮质连接的短束自动标记方法。标签为可视化和分析个体连接提供了有用的信息，如果没有其他额外信息，这是非常困难的。此外，我们提供了两种快速的体间束标记方法。获得的聚类可用于在个体或个体之间进行手动或自动连通性分析。
