The App uses the Gibbs unringing method to remove artifacts on diffusion-weighted magnetic resonance imaging data.

• Serge Koudoro
• Javier Ricardo Guaje Guerra

• Soichi Hayashi

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We ask that you the following articles when publishing papers that used data, code or other resources created by the brainlife.io community.

1. Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Dipy Contributors (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in neuroinformatics, 8, 8. https://doi.org/10.3389/fninf.2014.00008

2. Avesani, P., McPherson, B., Hayashi, S. et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). https://doi.org/10.1038/s41597-019-0073-y

You can see a list of Dipy Apps currently registered on Brainlife. Find the App that you'd like to run and click "Execute" tab to specify dataset that you'd like to run the App on.

To run this command, you can simply type:

singularity exec -e docker://brainlife/dipy:1.1.1 dipy_gibbs_ringing [your_args]

To see the documentation of all arguments, go to the following page

To see the documentation of all the arguments, follow this link.

All output files will be generated according to the passed arguments, as explained here.

This app runs on singularity.

• This is a Brainlife wrapper App stemming from the dipy_gibbs_ringing workflow.
• This single wrapper is exposed through an apps registered on Brainlife.io.
• More information about DIPY : https://dipy.org/.
• More information about the command line dipy_gibbs_ringing: Command line Reference.