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J Neurosci Methods. 2016 Nov 1;273:107-119. doi: 10.1016/j.jneumeth.2016.08.011. Epub 2016 Aug 24.

DiffusionKit: A light one-stop solution for diffusion MRI data analysis.

Author information

1
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
2
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: nmzuo@nlpr.ia.ac.cn.
3
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Abstract

BACKGROUND:

Diffusion magnetic resonance imaging (dMRI) techniques are receiving increasing attention due to their ability to characterize the arrangement map of white matter in vivo. However, the existing toolkits for dMRI analysis that have accompanied this surge possess noticeable limitations, such as large installation size, an incomplete pipeline, and a lack of cross-platform support.

NEW METHOD:

In this work, we developed a light, one-stop, cross-platform solution for dMRI data analysis, called DiffusionKit. It delivers a complete pipeline, including data format conversion, dMRI preprocessing, local reconstruction, white matter fiber tracking, fiber statistical analyses and various visualization schemes. Furthermore, DiffusionKit is a self-contained executable toolkit, without the need to install any other software.

RESULTS:

The DiffusionKit package is implemented in C/C++ and Qt/VTK, is freely available at http://diffusion.brainnetome.org and https://www.nitrc.org/projects/diffusionkit. The website of DiffusionKit includes test data, a complete tutorial and a series of tutorial examples. A mailing list has also been established for update notification and questions and answers.

COMPARISON WITH EXISTING METHODS:

DiffusionKit provides a full-function pipeline for dMRI data analysis, including data processing, modeling and visualization. Additionally, it provides both a graphical user interface (GUI) and command-line functions, which are helpful for batch processing. The standalone installation package has a small size and cross-platform support.

CONCLUSIONS:

DiffusionKit provides a complete pipeline with cutting-edge methods for dMRI data analysis, including both a GUI interface and command-line functions. The rich functions for both data analysis and visualization will facilitate and benefit dMRI research.

KEYWORDS:

Anatomical connectivity; DTI; Diffusion MRI; DiffusionKit; HARDI

PMID:
27568099
DOI:
10.1016/j.jneumeth.2016.08.011
[Indexed for MEDLINE]

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