Denoising of diffusion MRI using random matrix theory. Veraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., & Fieremans, E. NeuroImage, 142:394–406, November, 2016. doi abstract bibtex 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.
@article{veraart_denoising_2016,
title = {Denoising of diffusion {MRI} using random matrix theory},
volume = {142},
issn = {1095-9572},
doi = {10.1016/j.neuroimage.2016.08.016},
abstract = {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.},
language = {eng},
journal = {NeuroImage},
author = {Veraart, Jelle and Novikov, Dmitry S. and Christiaens, Daan and Ades-Aron, Benjamin and Sijbers, Jan and Fieremans, Els},
month = nov,
year = {2016},
pmid = {27523449},
pmcid = {PMC5159209},
keywords = {Accuracy, Data Interpretation, Statistical, Diffusion Magnetic Resonance Imaging, Humans, Image Processing, Computer-Assisted, Marchenko-Pastur distribution, PCA, Precision, Principal Component Analysis, White Matter},
pages = {394--406},
}
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