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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >FADTTSter: Accelerating Hypothesis Testing With Functional Analysis of Diffusion Tensor Tract Statistics
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FADTTSter: Accelerating Hypothesis Testing With Functional Analysis of Diffusion Tensor Tract Statistics

机译：Fadttster：通过扩散张量统计数据的功能分析加速假设检测

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摘要

Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) is a toolbox for analysis of white matter (WM) fiber tracts. It allows associating diffusion properties along major WM bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these WM tract properties. However, to use this toolbox, a user must have an intermediate knowledge in scripting languages (MATLAB). FADTTSter was created to overcome this issue and make the statistical analysis accessible to any non-technical researcher. FADTTSter is actively being used by researchers at the University of North Carolina. FADTTSter guides non-technical users through a series of steps including quality control of subjects and fibers in order to setup the necessary parameters to run FADTTS. Additionally, FADTTSter implements interactive charts for FADTTS' outputs. This interactive chart enhances the researcher experience and facilitates the analysis of the results. FADTTSter's motivation is to improve usability and provide a new analysis tool to the community that complements FADTTS. Ultimately, by enabling FADTTS to a broader audience, FADTTSter seeks to accelerate hypothesis testing in neuroimaging studies involving heterogeneous clinical data and diffusion tensor imaging. This work is submitted to the Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC*.
机译：弥散张量道统计的功能分析（FADTTS）是白质（WM）纤维束分析工具箱。它允许沿主要WM束扩散特性与一组感兴趣的协变量，如年龄，诊断状态和性别，以及这些WM道特性的变化的结构相关联。但是，要使用该工具箱，用户必须在脚本语言的中级知识（MATLAB）。 FADTTSter是为了克服这个问题，并作出任何非技术性的研究人员访问了统计分析。 FADTTSter正在被使用的研究人员在北卡罗莱纳大学。 FADTTSter通过一系列的步骤，包括的受试者和纤维质量控制，以建立必要的参数来运行FADTTS引导非技术用户。此外，FADTTSter实现了FADTTS”输出交互式图表。这种互动式图表增强了研究人员的经验和有利于结果的分析。 FADTTSter的动机是为了提高可用性和对社会，补充FADTTS提供新的分析工具。最终，通过使FADTTS更广泛的受众，FADTTSter旨在加速假设检验的神经影像学异质的临床数据和弥散张量成像研究。这项工作将被提交给分子，结构和功能成像会议的生物医学应用。此应用程序的源代码在NITTC *中可用。

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