dwidenoise

Synopsis

dMRI noise level estimation and denoising using Marchenko-Pastur PCA

Usage

dwidenoise [ options ]  dwi out
  • dwi: the input diffusion-weighted image.
  • out: the output denoised DWI image.

Description

DWI data denoising and noise map estimation by exploiting data redundancy in the PCA domain using the prior knowledge that the eigenspectrum of random covariance matrices is described by the universal Marchenko-Pastur (MP) distribution. Fitting the MP distribution to the spectrum of patch-wise signal matrices hence provides an estimator of the noise level ‘sigma’, as was first shown in Veraart et al. (2016) and later improved in Cordero-Grande et al. (2019). This noise level estimate then determines the optimal cut-off for PCA denoising.

Important note: image denoising must be performed as the first step of the image processing pipeline. The routine will fail if interpolation or smoothing has been applied to the data prior to denoising.

Note that this function does not correct for non-Gaussian noise biases present in magnitude-reconstructed MRI images. If available, including the MRI phase data can reduce such non-Gaussian biases, and the command now supports complex input data.

Options

  • -mask image Only process voxels within the specified binary brain mask image.
  • -extent window Set the patch size of the denoising filter. By default, the command will select the smallest isotropic patch size that exceeds the number of DW images in the input data, e.g., 5x5x5 for data with <= 125 DWI volumes, 7x7x7 for data with <= 343 DWI volumes, etc.
  • -noise level The output noise map, i.e., the estimated noise level ‘sigma’ in the data. Note that on complex input data, this will be the total noise level across real and imaginary channels, so a scale factor sqrt(2) applies.
  • -datatype float32/float64 Datatype for the eigenvalue decomposition (single or double precision). For complex input data, this will select complex float32 or complex float64 datatypes.
  • -estimator Exp1/Exp2 Select the noise level estimator (default = Exp2), either: * Exp1: the original estimator used in Veraart et al. (2016), or * Exp2: the improved estimator introduced in Cordero-Grande et al. (2019).

Standard options

  • -info display information messages.
  • -quiet do not display information messages or progress status; alternatively, this can be achieved by setting the MRTRIX_QUIET environment variable to a non-empty string.
  • -debug display debugging messages.
  • -force force overwrite of output files (caution: using the same file as input and output might cause unexpected behaviour).
  • -nthreads number use this number of threads in multi-threaded applications (set to 0 to disable multi-threading).
  • -config key value (multiple uses permitted) temporarily set the value of an MRtrix config file entry.
  • -help display this information page and exit.
  • -version display version information and exit.

References

Veraart, J.; Novikov, D.S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. & Fieremans, E. Denoising of diffusion MRI using random matrix theory. NeuroImage, 2016, 142, 394-406, doi: 10.1016/j.neuroimage.2016.08.016

Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping using random matrix theory. Magn. Res. Med., 2016, 76(5), 1582-1593, doi: 10.1002/mrm.26059

Cordero-Grande, L.; Christiaens, D.; Hutter, J.; Price, A.N.; Hajnal, J.V. Complex diffusion-weighted image estimation via matrix recovery under general noise models. NeuroImage, 2019, 200, 391-404, doi: 10.1016/j.neuroimage.2019.06.039


Author: Daan Christiaens (daan.christiaens@kcl.ac.uk) & Jelle Veraart (jelle.veraart@nyumc.org) & J-Donald Tournier (jdtournier@gmail.com)

Copyright: Copyright (c) 2016 New York University, University of Antwerp, and the MRtrix3 contributors

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