4.4 Article

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

Journal

JOURNAL OF NEUROSCIENCE METHODS
Volume 273, Issue -, Pages 107-119

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.jneumeth.2016.08.011

Keywords

Diffusion MRI; DTI; HARDI; Anatomical connectivity; DiffusionKit

Funding

  1. National Key Basic Research and Development Program (973) [2011CB707800]
  2. Strategic Priority Research Program of the Chinese Academy of Sciences [XDB02030300]
  3. Natural Science Foundation of China [81000634, 81270020]
  4. National High-tech RD Program (863) [2015AA020513]
  5. 16 NIH Institutes and Centers [1U54MH091657]
  6. McDonnell Center for Systems Neuroscience at Washington University

Ask authors/readers for more resources

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. (C) 2016 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available