dipy_gibbs_ringing

Usage

dipy_gibbs_ringing [-h] [–slice_axis int] [–n_points int] [–num_processes int] [–out_dir str] [–out_unring str] input_files

Workflow for applying Gibbs Ringing method.

Positional Arguments

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

options:

-h, --help

show this help message and exit

--slice_axis int

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 int

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

--num_processes int

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 Arguments(Optional)

--out_dir str

Output directory. (default current directory)

--out_unring str

Name of the resulting denoised volume.

References

[1]

Neto Henriques, R., 2018. Advanced Methods for Diffusion MRIData Analysis and their Application to the Healthy Ageing Brain(Doctoral thesis). https://doi.org/10.17863/CAM.29356

[2]

Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringingartifact removal based on local subvoxel-shifts. Magn Reson Med. 2016doi: 10.1002/mrm.26054.

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.