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].

• --b0_threshold

  Threshold used to find b0 volumes.

• --bvecs_tol

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

• --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.

• --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`.

Output Options

• --out_dir

  Output directory. (default current directory)

• --out_denoised

  Name of the resulting denoised 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]

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.