Denoise DWI data and estimate the noise level based on the optimal threshold for PCA
dwidenoise [ options ] dwi out
- dwi: the input diffusion-weighted image.
- out: the output denoised DWI image.
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 distribution.
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.
- -mask image only perform computation within the specified binary brain mask image.
- -extent window set the window size of the denoising filter. (default = 5,5,5)
- -noise level the output noise map.
- -info display information messages.
- -quiet do not display information messages or progress status.
- -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)
- -failonwarn terminate program if a warning is produced
- -help display this information page and exit.
- -version display version information and exit.
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
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