Denoising of diffusion MRI using random matrix theory

By Jelle Veraart, Dmitry S. Novikov, Daan Christiaens, Benjamin Ades-aron, Jan Sijbers, Els Fieremans. NeuroImage, 2016

1,773 citations recorded by the source corpus.

We introduce and evaluate a post-processing technique for fast denoising diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements, yielding parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and/or spatial resolution.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2fb7811eed842bf44eb28491f052f3cd37237249

How researchers see this paper

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In Radiology, Nuclear Medicine and Imaging

1. Joseph V. Hajnal
2. Daan Christiaens
3. Anthony N. Price
4. Arvid Lundervold

Citations in the corpus

• Complex diffusion-weighted image estimation via matrix recovery under general noise models (2018, positive): “Consequently, we propose an extension of the random matrix denoising method in [9], termed MarčenkoPastur PCA (MPPCA).”