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Niimath和Fslmaths:将复制作为增强流行神经影像工具的一种方法。

niimath and fslmaths: replication as a method to enhance popular neuroimaging tools.

作者信息

Rorden Christopher, Webster Matthew, Drake Chris, Jenkinson Mark, Clayden Jonathan D, Li Ningfei, Hanayik Taylor

机构信息

McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia SC 29016, USA.

Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

出版信息

Apert Neuro. 2024;4. doi: 10.52294/001c.94384. Epub 2024 Mar 8.

Abstract

Neuroimaging involves the acquisition of extensive 3D images and 4D time series data to gain insights into brain structure and function. The analysis of such data necessitates both spatial and temporal processing. In this context, "fslmaths" has established itself as a foundational software tool within our field, facilitating domain-specific image processing. Here, we introduce "niimath," a clone of fslmaths. While the term "clone" often carries negative connotations, we illustrate the merits of replicating widely-used tools, touching on aspects of licensing, performance optimization, and portability. For instance, our work enables the popular functions of fslmaths to be disseminated in various forms, such as a high-performance compiled R package known as "imbibe", a Windows executable, and a WebAssembly plugin compatible with JavaScript. This versatility is demonstrated through our NiiVue live demo web page. This application allows 'edge computing' where image processing can be done with a zero-footprint tool that runs on any web device without requiring private data to be shared to the cloud. Furthermore, our efforts have contributed back to FSL, which has integrated the optimizations that we've developed. This synergy has enhanced the overall transparency, utility and efficiency of tools widely relied upon in the neuroimaging community.

摘要

神经成像涉及获取大量的3D图像和4D时间序列数据,以深入了解大脑结构和功能。对此类数据的分析需要进行空间和时间处理。在这种背景下,“fslmaths”已成为我们领域内的一个基础软件工具,促进特定领域的图像处理。在此,我们介绍“niimath”,它是fslmaths的一个克隆版本。虽然“克隆”一词通常带有负面含义,但我们阐述了复制广泛使用的工具的优点,涉及许可、性能优化和可移植性等方面。例如,我们的工作使fslmaths的常用功能能够以各种形式传播,比如一个名为“imbibe”的高性能编译R包、一个Windows可执行文件以及一个与JavaScript兼容的WebAssembly插件。这种多功能性通过我们的NiiVue实时演示网页得到了展示。该应用程序允许进行“边缘计算”,即可以使用一个零占用空间的工具在任何网络设备上进行图像处理,而无需将私人数据共享到云端。此外,我们的工作也回馈给了FSL,FSL已经整合了我们所开发的优化内容。这种协同作用提高了神经成像社区广泛依赖的工具的整体透明度、实用性和效率。

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