Nighres: processing tools for high-resolution neuroimaging
Nighres: processing tools for high-resolution neuroimaging
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DOI：
10.1093/gigascience/giy082
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发表时间：
2018-07-04
期刊：
影响因子：
9.2
通讯作者：
Bazin, Pierre-Louis
中科院分区：
文献类型：
--
作者：
Huntenburg, Julia M.;Steele, Christopher J.;Bazin, Pierre-Louis
随着超高场人体磁共振成像 (MRI) 的最新改进，在给定 MRI 实验中每个受试者收集的数据量显着增加。标准图像处理包经常受到这些数据大小的挑战。需要专门的方法来利用其非凡的空间分辨率。在这里，我们介绍一个灵活的 Python 工具箱，它实现了一组用于高分辨率神经成像的先进技术。借助这些工具，可以在合理的时间内以高达 500 μm 的分辨率对皮质 MRI 数据进行分割和层状分析。全面的在线文档使该工具箱易于使用和安装。内容广泛的开发人员指南鼓励其他研究人员做出贡献，这将有助于加速高分辨率神经成像这一前景广阔的领域的进展。
With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to leverage their extraordinary spatial resolution. Here, we introduce a flexible Python toolbox that implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 mu m in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer's guide encourages contributions from other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging.