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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| Published in: | NeuroImage (Orlando, Fla.) Vol. 226; p. 117539 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.02.2021
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
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| Abstract | •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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| AbstractList | 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. •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. 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.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. 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. |
| ArticleNumber | 117539 |
| Author | Uğurbil, Kamil Yacoub, Essa Akçakaya, Mehmet Moeller, Steen Pisharady, Pramod Kumar Dowdle, Logan Lenglet, Christophe Ramanna, Sudhir Wu, Xiaoping |
| AuthorAffiliation | a Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA b Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA |
| AuthorAffiliation_xml | – name: a Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – name: b Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA |
| Author_xml | – sequence: 1 givenname: Steen orcidid: 0000-0003-1698-7260 surname: Moeller fullname: Moeller, Steen email: moeller@cmrr.umn.edu organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 2 givenname: Pramod Kumar surname: Pisharady fullname: Pisharady, Pramod Kumar organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 3 givenname: Sudhir surname: Ramanna fullname: Ramanna, Sudhir organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 4 givenname: Christophe surname: Lenglet fullname: Lenglet, Christophe organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 5 givenname: Xiaoping surname: Wu fullname: Wu, Xiaoping organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 6 givenname: Logan orcidid: 0000-0002-1879-705X surname: Dowdle fullname: Dowdle, Logan organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 7 givenname: Essa surname: Yacoub fullname: Yacoub, Essa organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 8 givenname: Kamil orcidid: 0000-0002-8475-9334 surname: Uğurbil fullname: Uğurbil, Kamil organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA – sequence: 9 givenname: Mehmet surname: Akçakaya fullname: Akçakaya, Mehmet organization: Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33186723$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 Copyright © 2020. Published by Elsevier Inc. Copyright Elsevier Limited Feb 1, 2021 |
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| DOI | 10.1016/j.neuroimage.2020.117539 |
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| Keywords | Brain imaging Human connectome project Multiband Diffusion MRI Simultaneous multi-slice Denoising Singular value decomposition |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2020. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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. |
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| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2021 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
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| Snippet | •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... Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However,... |
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| SubjectTerms | 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 |
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| Title | NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing |
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| Volume | 226 |
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