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      Multi-channel framelet denoising of diffusion-weighted images

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          Abstract

          Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an 0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.

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          Most cited references 25

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          A Review of Image Denoising Algorithms, with a New One

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            Adaptive non-local means denoising of MR images with spatially varying noise levels.

            To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images. (c) 2009 Wiley-Liss, Inc.
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              Identification of MCI individuals using structural and functional connectivity networks.

              Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: VisualizationRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: ValidationRole: Writing – original draft
                Role: ConceptualizationRole: Methodology
                Role: ConceptualizationRole: Funding acquisitionRole: Methodology
                Role: Funding acquisitionRole: Project administrationRole: ResourcesRole: Supervision
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                6 February 2019
                : 14
                : 2
                Affiliations
                [1 ] Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
                [2 ] School of Information and Electrical Engineering, Hunan University of Science & Technology, Xiangtan, China
                [3 ] Vancouver Research Center, Huawei, Burnaby, Canada
                [4 ] Beijing International Center for Mathematical Research, Peking University, Beijing, China
                [5 ] Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
                Center for Neuroscience and Regenerative Medicine, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-18-14820
                10.1371/journal.pone.0211621
                6364918
                30726257
                © 2019 Chen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 12, Tables: 2, Pages: 19
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: NS093842 EB022880
                Award Recipient :
                Funded by: Foundation for the Hunan Provincial Education Department (China)
                Award ID: 15A066
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: EB022880, EB006733, EB009634, AG041721, and MH100217
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 11671022
                Award Recipient :
                This work was supported in part by NIH grants (NS093842 to Pew-Thian Yap, EB022880 to Pew-Thian Yap and Dinggang Shen, EB006733 to Dinggang Shen, EB009634 to Dinggang Shen, AG041721 to Dinggang Shen, and MH100217 to Dinggang Shen) and Hunan Provincial Education Department grant (15A066) to Jian Zhang. Bin Dong was supported in part by NSFC 11671022.
                Categories
                Research Article
                Research and Analysis Methods
                Imaging Techniques
                Engineering and Technology
                Signal Processing
                Noise Reduction
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Brain Morphometry
                Diffusion Magnetic Resonance Imaging
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Brain Morphometry
                Diffusion Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Brain Morphometry
                Diffusion Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Brain Morphometry
                Diffusion Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Brain Morphometry
                Diffusion Magnetic Resonance Imaging
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Brain Morphometry
                Diffusion Magnetic Resonance Imaging
                Engineering and Technology
                Signal Processing
                Image Processing
                Biology and Life Sciences
                Anatomy
                Nervous System
                Central Nervous System
                Medicine and Health Sciences
                Anatomy
                Nervous System
                Central Nervous System
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Mathematical Functions
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Custom metadata
                The synthetic data used in this study can be found at https://osf.io/57gcx/. The real data cannot be made publicly available as no such authorization was given by the Local Ethical Committee. Data are available upon request (contact with UNC IRB via irb_questions@ 123456unc.edu ) to researchers qualified to handle confidential data.

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