Denoising of diffusion MRI using random matrix theory
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We introduce and evaluate a post-processing technique for fast denoising of 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. This yields 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 spatial resolution.
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MRtrix3 offers image analysis methods less specific to dMRI, including image filtering and calculation, statistics, image denoising, Gibbs ringing suppression, intensity normalisation, and diffeomorphic image registration.
“MRtrix3 also offers image analysis methods less specific to dMRI such as image filtering and calculation, statistics, image denoising (Veraart et al., 2016) and Gibbs ringing suppression (Kellner et al., 2016) , intensity normalisation (Raffelt et al., 2017a) and diffeomorphic image registration (Raffelt et al., 2011) .”
From MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation (2019) · cites this paper as Veraart et al., 2016
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Self-citation
- Authors
- Veraart, J., Novikov, D. S., Christiaens, D., Ades-aron, B., Sijbers, J., & Fieremans, E.
- Affiliations
- University of Antwerp (BE)
- New York University (US)
- KU Leuven (BE)
- Corresponding author
- Veraart, Jelle
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 142
- Pages
- 394-406
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5159209
- OpenAlex
- W2508982726
- Cited by
- 1,905
- References
- 3
- Citations by year
- 2026: 243 · 2025: 336 · 2024: 268 · 2023: 230 · 2022: 252 · 2021: 235 · 2020: 157 · 2019: 108 · 2018: 53 · 2017: 20 · 2013: 1 · 2012: 1
- FWCI
- 52.38
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Lirias (submittedVersion, cc-by-nc-nd)
- Funders
- Raymond and Beverly Sackler Laboratories for Convergence of Physical, Engineering and Biomedical Sciences
- Litwin Foundation for Alzheimer's Research
- National Institute of Neurological Disorders and Stroke (R01NS088040)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- diffusion MRI, image denoising, random matrix theory, signal-to-noise ratio improvement, diffusion parameter estimation, fiber orientation estimation, thermal noise
- MeSH terms
- Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Principal Component Analysis, Diffusion Magnetic Resonance Imaging, White Matter
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