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UNC-Utah NA-MIC 弥散张量纤维束分析框架。

UNC-Utah NA-MIC framework for DTI fiber tract analysis.

机构信息

Neuro Image Research and Analysis Laboratory, Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA.

Neuro Image Research and Analysis Laboratory, Department of Psychiatry, University of North Carolina Chapel Hill, NC, USA ; Children's Hospital of Pittsburgh, University of Pittsburgh Pittsburgh, PA, USA.

出版信息

Front Neuroinform. 2014 Jan 9;7:51. doi: 10.3389/fninf.2013.00051. eCollection 2014.

Abstract

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

摘要

弥散张量成像(DTI)已经成为神经影像学领域中的一种重要模态,用于捕捉微观结构的变化,并评估白质的完整性或发育情况。虽然存在许多示踪工具集,但这些工具通常缺乏用于预处理或分析纤维束中扩散特性的工具。目前,该领域迫切需要一个用于执行沿纤维束分析的连贯的端到端工具集,供非技术神经影像学研究人员使用。UNC-Utah NA-MIC DTI 框架是一个连贯的、开源的、端到端的基于图谱纤维束的 DTI 分析工具集,涵盖了 DICOM 数据转换、质量控制、图谱构建、纤维追踪、纤维参数化以及扩散特性的统计分析。大多数步骤都利用图形用户界面(GUI)来简化交互,并为非技术研究人员/调查人员提供广泛的 DTI 分析框架。我们在一项小型的、横断面的神经影像学研究中展示了我们框架的使用情况,该研究涉及来自婴儿脑成像研究(IBIS)网络的 8 名健康 1 岁儿童。在这项有限的测试研究中,我们通过量化 1 岁时胼胝体和穹窿纤维束的扩散特性,说明了我们方法的强大功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1758/3885811/696bec45db9a/fninf-07-00051-g001.jpg

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