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FiberStars: Visual Comparison of Diffusion Tractography Data between Multiple Subjects

机译:Fiberstars:多个科目之间的扩散牵引数据的视觉比较

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Tractography from high-dimensional diffusion magnetic resonance imaging (dMRI) data allows brain's structural connectivity analysis. Recent dMRI studies aim to compare connectivity patterns across subject groups and disease populations to understand subtle abnormalities in the brain's white matter connectivity and distributions of biologically sensitive dMRI derived metrics. Existing software products focus solely on the anatomy, are not intuitive or restrict the comparison of multiple subjects. In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive visualization of brain fiber clusters combining existing 3D anatomy with compact 2D visualizations. With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers using different views. To evaluate the usability of our software, we performed a quantitative user study. We asked domain experts and non-experts to find patterns in a tractography dataset with either FiberStars or an existing dMRI exploration tool. Our results show that participants using FiberStars can navigate extensive collections of tractography faster and more accurately. All our research, software, and results are available openly.
机译:来自高维扩散磁共振成像(DMRI)数据的牵引装置允许大脑的结构连接分析。最近的DMRI研究旨在比较主体群体和疾病群体的连接模式,以了解大脑白质连通性和生物敏感DMRI衍生度量分布的微妙异常。现有的软件产品仅关注解剖结构,不直观或限制多个受试者的比较。在本文中,我们介绍了Fiberstars的设计和实现,一种用于牵引数据的视觉分析工具,允许将现有的3D解剖结构与紧凑的2D可视化组合的脑光纤集群的交互式可视化。使用Fiberstars,研究人员可以使用不同的观点来分析和比较大量脑纤维的多个受试者。为了评估软件的可用性,我们执行了定量用户学习。我们要求域名专家和非专家在具有Fiberstar或现有的DMRI探索工具中找到牵引数据集中的模式。我们的结果表明,使用Fiberstars的参与者可以更快,更准确地导航大量牵引牵引。我们所有的研究,软件和结果都可以公开提供。

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