dipy_gibbs_ringing

Synopsis

Workflow for applying Gibbs Ringing method.

See [1] and [2] for further details about the method.

Usage

dipy_gibbs_ringing [OPTIONS] input_files

Input Parameters

• input_files

  Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

General Options

• --slice_axis

  Data axis corresponding to the number of acquired slices. Could be (0, 1, or 2): for example, a value of 2 would mean the third axis.

• --n_points

  Number of neighbour points to access local TV (see note).

• --num_processes

  Split the calculation to a pool of children processes. Only applies to 3D or 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

Output Options

• --out_dir

  Output directory. (default current directory)

• --out_unring

  Name of the resulting denoised volume.

References

[1]

Elias Kellner, Bibek Dhital, Valerij G. Kiselev, and Marco Reisert. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 76(5):1574–1581, 2016. URL: https://doi.org/10.1002/mrm.26054, doi:10.1002/mrm.26054.

[2]

Rafael Neto Henriques. Advanced Methods for Diffusion MRI Data Analysis and their Application to the Healthy Ageing Brain. PhD thesis, Cambridge University, Cambridge, United Kingdom, 2018. URL: https://doi.org/10.17863/CAM.29356, doi:10.17863/CAM.29356.

Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.