Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER)

Benjamin Ades-Aron^{1}, Jelle Veraart^{1,2}, Elias Kellner^{3}, Yvonne W. Lui^{1}, Dmitry S. Novikov^{1}, and Els Fieremans^{1}

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2. Veraart, J., Fieremans, E., Jelescu, I. O., Knoll, F., & Novikov, D. S. (2015). Gibbs ringing in diffusion MRI. Magn. Reson. Med. In Press, published online.DOI: 10.1002/mrm.25866

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Comparison
between different DKI post-processing algorithms. The main difference between
the state-of-the-art pipeline and the newly proposed DESIGNER pipeline
is the substitution of smoothing and constrained fitting for model independent
noise estimation using the Marchenko-Pastur distribution and automatic Gibbs
artifact removal. MK maps generated by the two pipelines as well as
MK generated from unconstrained fitting of the original uncorrected dMRI images are shown as an illustration.

Left:
residual noise map produced after the denoising step of the post-processing
routine. Middle: residual noise map left after isotropic Gaussian smoothing.
Right: Difference between denoising techniques. The rightmost image shows that
low pass filtering kills anatomic signal that is preserved using the Marchenko-Pastur
noise removal.

Histograms
of residual differences between raw dMRI and each processing step show how each denoising
routine affects the distribution of noise in the dataset. The left image
shows $$$b = 0$$$ data and the right image shows $$$b = 2000\ \mathrm{s/mm}^{2}$$$ data
for a single direction. Median values for the $$$b = 0$$$ data are $$$-3.559, 0.3901,\ \mathrm{and}\ 0.1492$$$ for the smoothed, Gibbs corrected and denoised images respectively.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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