dipy_denoise_lpca

Synopsis

Workflow wrapping LPCA denoising method.

See [1] for further details about the method.

Usage

dipy_denoise_lpca [OPTIONS] input_files bvalues_files bvectors_files

Input Parameters

• input_files

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

• bvalues_files

  Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

• bvectors_files

  Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

General Options

• --sigma

  Standard deviation of the noise estimated from the data. 0 means sigma value estimation following the algorithm in Manjón et al.[2]. (default: 0)

• --b0_threshold

  Threshold used to find b0 volumes. (default: 50)

• --bvecs_tol

  Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors. (default: 0.01)

• --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)

• --tau_factor

  Thresholding of PCA eigenvalues is done by nulling out eigenvalues that are smaller than:

  \[\tau = (\tau_{factor} \sigma)^2\]

  \(\tau_{factor}\) can be change to adjust the relationship between the noise standard deviation and the threshold \(\tau\). If \(\tau_{factor}\) is set to None, it will be automatically calculated using the Marcenko-Pastur distribution :footcite:p`Veraart2016b`. (default: 2.3)

Output Options

• --out_dir

  Output directory. (default: current directory)

• --out_denoised

  Name of the resulting denoised volume. (default: dwi_lpca.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]

José V. Manjón, Pierrick Coupé, Luis Concha, Antonio Buades, D. Louis Collins, and Montserrat Robles. Diffusion Weighted Image Denoising Using Overcomplete Local PCA. PLOS ONE, 8(9):e73021, 2013. URL: https://doi.org/10.1371/journal.pone.0073021, doi:10.1371/journal.pone.0073021.

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.