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DWI denoising

MRtrix includes a command dwidenoise, which implements DWI data denoising and noise map estimation by exploiting data redundancy in the PCA domain ([Veraart2016a]_ and [Veraart2016b]_). The method uses the prior knowledge that the eigenspectrum of random covariance matrices is described by the universal Marchenko Pastur distribution.

Recommended use

Image denoising must be performed as the first step of the image-processing pipeline. Interpolation or smoothing in other processing steps, such as motion and distortion correction, may alter the noise characteristics and thus violate the assumptions upon which MP-PCA is based.

Typical use will be:

dwidenoise dwi.mif out.mif -noise noise.mif

where dwi.mif contains the raw input DWI image, out.mif is the denoised DWI output, and noise.mif is the estimated spatially-varying noise level.

We always recommend eyeballing the residuals, i.e. out - in, as part of the quality control. The lack of anatomy in the residual maps is a marker of accuracy and signal-preservation during denoising. The residuals can be easily obtained with

mrcalc dwi.mif out.mif -subtract res.mif
mrview res.mif

The kernel size, default 5x5x5, can be chosen by the user (option: -extent). For maximal SNR gain we suggest to choose N>M for which M is typically the number of DW images in the data (single or multi-shell), where N is the number of kernel elements. However, in case of spatially varying noise, it might be beneficial to select smaller sliding kernels, e.g. N~M, to balance between precision, accuracy, and resolution of the noise map.

Note that this function does not correct for non-Gaussian noise biases yet.