NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing

•We propose a framework, NORDIC, for denoising complex valued dMRI data using Gaussian statistics.•The main feature of the proposed method is to only remove signal components which cannot be distinguished from thermal noise.•Quantitative evaluation of NORDIC is performed across different resolutions...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 226; S. 117539
Hauptverfasser: Moeller, Steen, Pisharady, Pramod Kumar, Ramanna, Sudhir, Lenglet, Christophe, Wu, Xiaoping, Dowdle, Logan, Yacoub, Essa, Uğurbil, Kamil, Akçakaya, Mehmet
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.02.2021
Elsevier Limited
Elsevier
Schlagworte:
Algorithms
Brain
Brain - anatomy & histology
Brain imaging
Connectome - methods
Denoising
Diffusion
Diffusion Magnetic Resonance Imaging - methods
Diffusion MRI
Human connectome project
Humans
Image Processing, Computer-Assisted - methods
Magnetic resonance imaging
Methods
Multiband
Neuroimaging
Noise
Noise reduction
Principal components analysis
Signal-To-Noise Ratio
Simultaneous multi-slice
Singular value decomposition
Values
Brain imaging
Human connectome project
Multiband
Diffusion MRI
Simultaneous multi-slice
Denoising
Singular value decomposition
ISSN:1053-8119, 1095-9572, 1095-9572
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Beschreibung
Zusammenfassung:•We propose a framework, NORDIC, for denoising complex valued dMRI data using Gaussian statistics.•The main feature of the proposed method is to only remove signal components which cannot be distinguished from thermal noise.•Quantitative evaluation of NORDIC is performed across different resolutions and SNR using human Connectome type acquisitions.•The proposed method outperforms a state-of-art methods for denoising dMRI in terms of fiber orientation dispersion.•Up to 6 fold improvement in apparent SNR for 0.9mm whole brain dMRI at 3T. Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method.
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Credit authorship contribution statement
Steen Moeller: Conceptualization, Software, Investigation, Methodology, Formal analysis, Writing - original draft, Visualization. Pramod Kumar Pisharady: Methodology, Formal analysis, Investigation, Visualization, Writing - review & editing. Sudhir Ramanna: Investigation. Christophe Lenglet: Methodology. Xiaoping Wu: Validation. Logan Dowdle: Formal analysis. Essa Yacoub: Resources, Funding acquisition. Kamil Uğurbil: Resources, Supervision, Methodology, Writing - review & editing, Funding acquisition. Mehmet Akçakaya: Conceptualization, Methodology, Writing - review & editing, Funding acquisition.
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2020.117539