dipy_denoise_mppca

Synopsis

Workflow wrapping Marcenko-Pastur PCA denoising method.

See [1] for further details about the method.

Usage

dipy_denoise_mppca [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

• --patch_radius

  The radius of the local patch to be taken around each voxel (in voxels) For example, for a patch radius with value 2, and assuming the input image is a 3D image, the denoising will take place in blocks of 5x5x5 voxels. (default: 2)

• --pca_method

  Use either eigenvalue decomposition (‘eig’) or singular value decomposition (‘svd’) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate. (default: eig)

• --return_sigma

  If true, a noise standard deviation estimate based on the Marcenko-Pastur distribution is returned [2]. (default: False)

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_denoised

  Name of the resulting denoised volume. (default: dwi_mppca.nii.gz)

• --out_sigma

  Name of the resulting sigma volume. (default: dwi_sigma.nii.gz)

References

[1]

Jelle Veraart, Els Fieremans, Ileana O. Jelescu, Florian Knoll, and Dmitry S. Novikov. Gibbs ringing in diffusion MRI. Magnetic Resonance in Medicine, 76(1):301–314, July 2016. URL: https://doi.org/10.1002/mrm.25866, doi:10.1002/mrm.25866.

[2]

Jelle Veraart, Els Fieremans, and Dmitry S. Novikov. Diffusion MRI noise mapping using random matrix theory. Magnetic Resonance in Medicine, 76(5):1582–1593, November 2016. URL: https://doi.org/10.1002/mrm.26059, doi:10.1002/mrm.26059.

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.