Jordan Kesshi M, Amirbekian Bagrat, Keshavan Anisha, Henry Roland G
UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco and Berkeley, CA.
Departments of Neurology, University of California, San Francisco, CA.
J Neuroimaging. 2018 Jan;28(1):64-69. doi: 10.1111/jon.12467. Epub 2017 Sep 21.
Diffusion-weighted magnetic resonance imaging tractography can be used to create models of white matter fascicles. Anatomical and pathological variability between subjects can drastically alter the tractography output, so standardizing results across a cohort is nontrivial. Furthermore, tractography methods have inherently low reproducibility due to stochasticity (for probabilistic methods) and subjective decisions, since the final fascicle model often requires a manual intervention step performed by an expert human operator to control both outliers and systematic false-positive pathways, as defined by prior knowledge of anatomy.
We present an approach that computationally assigns a cluster confidence index (CCI) reflecting the reproducibility of that pathway in the context of a streamline dataset. This metric is a tractography algorithm-agnostic tool that can be applied to any dataset of streamlines.
Applications of this metric include systematic elimination of outlier streamlines using a CCI threshold and interactive filtering by CCI to facilitate manual segmentation of fascicle models.
This method is intended to replace the application of a streamline density threshold so that outliers are eliminated based on low pathway density instead of voxel-wise density.
扩散加权磁共振成像纤维束成像可用于创建白质纤维束模型。个体之间的解剖学和病理学差异会极大地改变纤维束成像的输出结果，因此在一个队列中标准化结果并非易事。此外，由于随机性（对于概率性方法）和主观决策，纤维束成像方法本身的可重复性较低，因为最终的纤维束模型通常需要由专业人员进行人工干预步骤，以控制异常值和系统性假阳性路径，这些是由解剖学的先验知识定义的。
我们提出了一种方法，该方法通过计算为每个路径分配一个聚类置信指数（CCI），以反映该路径在流线数据集背景下的可重复性。该指标是一种与纤维束成像算法无关的工具，可应用于任何流线数据集。
该指标的应用包括使用CCI阈值系统地消除异常流线，以及通过CCI进行交互式过滤，以促进纤维束模型的手动分割。
该方法旨在取代流线密度阈值的应用，从而基于低路径密度而非体素密度来消除异常值。
