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.

• --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.

• --return_sigma

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

Output Options

• --out_dir

  Output directory. (default current directory)

• --out_denoised

  Name of the resulting denoised volume.

• --out_sigma

  Name of the resulting sigma volume.

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.