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noddi matlab toolbox  (MathWorks Inc)


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    MathWorks Inc noddi matlab toolbox
    Overview of diffusion MRI models evaluated in this study. Schematic illustration of the diffusion modeling frameworks, including <t>DTI,</t> <t>DKI,</t> SMT, <t>NODDI</t> and SMI. The diagram highlights key modeling assumptions and acquisition requirements, with DTI based on single-shell diffusion MRI and the remaining models estimated from multi-shell data. Representative voxel-wise parametric maps are shown in the lower panel to illustrate model-dependent contrast across white matter. AD , axial diffusivity; MK , mean kurtosis; AK , axial kurtosis; V ax intra-neurite volume fraction; D ax , intra-neurite axial diffusivity; ficvf , intracellular volume fraction; odi , orientation dispersion index; fiso, isotropic volume fraction; f , intra-axonal volume fraction; and , extra-axonal perpendicular and parallel diffusivities.
    Noddi Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2341 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+noddi+toolbox/Database+Toolbox/med_rxiv__64898__2026__03__15__26348428-48-29-30
    Average 96 stars, based on 2341 article reviews
    noddi matlab toolbox - by Bioz Stars, 2026-10
    96/100 stars

    Images

    1) Product Images from "Comparative Evaluation of Microstructural Diffusion Methods in Characterizing Multiple Sclerosis Lesions: The Importance of multi-b shells acquisition"

    Article Title: Comparative Evaluation of Microstructural Diffusion Methods in Characterizing Multiple Sclerosis Lesions: The Importance of multi-b shells acquisition

    Journal: medRxiv

    doi: 10.64898/2026.03.15.26348428

    Overview of diffusion MRI models evaluated in this study. Schematic illustration of the diffusion modeling frameworks, including DTI, DKI, SMT, NODDI and SMI. The diagram highlights key modeling assumptions and acquisition requirements, with DTI based on single-shell diffusion MRI and the remaining models estimated from multi-shell data. Representative voxel-wise parametric maps are shown in the lower panel to illustrate model-dependent contrast across white matter. AD , axial diffusivity; MK , mean kurtosis; AK , axial kurtosis; V ax intra-neurite volume fraction; D ax , intra-neurite axial diffusivity; ficvf , intracellular volume fraction; odi , orientation dispersion index; fiso, isotropic volume fraction; f , intra-axonal volume fraction; and , extra-axonal perpendicular and parallel diffusivities.
    Figure Legend Snippet: Overview of diffusion MRI models evaluated in this study. Schematic illustration of the diffusion modeling frameworks, including DTI, DKI, SMT, NODDI and SMI. The diagram highlights key modeling assumptions and acquisition requirements, with DTI based on single-shell diffusion MRI and the remaining models estimated from multi-shell data. Representative voxel-wise parametric maps are shown in the lower panel to illustrate model-dependent contrast across white matter. AD , axial diffusivity; MK , mean kurtosis; AK , axial kurtosis; V ax intra-neurite volume fraction; D ax , intra-neurite axial diffusivity; ficvf , intracellular volume fraction; odi , orientation dispersion index; fiso, isotropic volume fraction; f , intra-axonal volume fraction; and , extra-axonal perpendicular and parallel diffusivities.

    Techniques Used: Diffusion-based Assay, Dispersion

    Group comparisons of diffusion metrics across five white matter tissue types in MS and HC. Tissue classes included cBHs (chronic black holes), T2-lesions, cBHs-NAWM and T2-NAWM (normal-appearing white matter), and NWM (normal white matter). Diffusion metrics were derived from DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), SMT (intra-axonal signal fraction [ V ax ], extra-axonal diffusivity [ D ex ]), NODDI (intracellular volume fraction [ ficvf ], isotropic volume fraction [ fiso ], orientation dispersion index [ odi ], kappa ), and SMI (intra-axonal fraction [ f ], intra-axonal diffusivity [ D a ], extra-axonal parallel diffusivity [ ], extra-axonal perpendicular diffusivity [ ], fiber orientation coherence [ p 2 ]).
    Figure Legend Snippet: Group comparisons of diffusion metrics across five white matter tissue types in MS and HC. Tissue classes included cBHs (chronic black holes), T2-lesions, cBHs-NAWM and T2-NAWM (normal-appearing white matter), and NWM (normal white matter). Diffusion metrics were derived from DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), SMT (intra-axonal signal fraction [ V ax ], extra-axonal diffusivity [ D ex ]), NODDI (intracellular volume fraction [ ficvf ], isotropic volume fraction [ fiso ], orientation dispersion index [ odi ], kappa ), and SMI (intra-axonal fraction [ f ], intra-axonal diffusivity [ D a ], extra-axonal parallel diffusivity [ ], extra-axonal perpendicular diffusivity [ ], fiber orientation coherence [ p 2 ]).

    Techniques Used: Diffusion-based Assay, Derivative Assay, Dispersion

    Related Articles

    other:

    Article Title: Visual Snow Syndrome Improves With Modulation of Resting-State Functional MRI Connectivity After Mindfulness-Based Cognitive Therapy: An Open-Label Feasibility Study
    Article Snippet: DTI and NODDI models were fitted to the DWI data, after eddy current, distortion, and motion correction, using FSL10 and the MATLAB (MathWorks, Inc, Natick, MA) NODDI toolboxes (http://nitrc.org/projects/noddi_ toolbox).

    Article Title: Visual Snow Syndrome Improves With Modulation of Resting-State Functional MRI Connectivity After Mindfulness-Based Cognitive Therapy: An Open-Label Feasibility Study
    Article Snippet: DTI and NODDI models were fitted to the DWI data, after eddy current, distortion, and motion correction, using FSL and the MATLAB (MathWorks, Inc, Natick, MA) NODDI toolboxes ( http://nitrc.org/projects/noddi_toolbox ).

    Article Title: Optimizing the intrinsic parallel diffusivity in NODDI: An extensive empirical evaluation.
    Article Snippet: For each of the 26 values, the model was fitted to the measured dMRI signal voxel by voxel using the Matlab (The MathWorks, Inc., Natick, MA) NODDI toolbox (http://nitrc.org/projects/noddi_toolbox).

    Diffusion-based Assay:

    Article Title: Integrating Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging to Improve the Predictive Capabilities of CED Models
    Article Snippet: .. The NODDI model was fitted to all the volumes of the two-shell DMRI datasets using the MATLAB NODDI toolbox ( http://mig.cs.ucl.ac.uk/Tutorial.NODDImatlab ), that computed the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{VF}}_{\text{INC}}$$\end{document} VF INC and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{VF}}_{\text{Water}}$$\end{document} VF Water diffusion compartments of each voxel. ..

    Article Title: Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors
    Article Snippet: .. Once preprocessing was completed, the Watson‐NODDI model was fitted to the two‐shell dMRI datasets (NODDI acquisition: 60 directions at b ‐value 3,000 s/mm 2 , 35 directions at b ‐value 711 s/mm 2 , 11 B0 volumes) using the MATLAB NODDI toolbox ( http://mig.cs.ucl.ac.uk/Tutorial.NODDImatlab ) to extract the following NODDI maps (Figure ): voxel fraction of Gaussian anisotropic diffusion (extracellular volume fraction [FECV]), voxel fraction of non‐Gaussian anisotropic diffusion (intracellular volume fraction [FICV]), voxel fraction of isotropic Gaussian diffusion (FISO), and orientation dispersion index (ODI) maps. ..

    Dispersion:

    Article Title: Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors
    Article Snippet: .. Once preprocessing was completed, the Watson‐NODDI model was fitted to the two‐shell dMRI datasets (NODDI acquisition: 60 directions at b ‐value 3,000 s/mm 2 , 35 directions at b ‐value 711 s/mm 2 , 11 B0 volumes) using the MATLAB NODDI toolbox ( http://mig.cs.ucl.ac.uk/Tutorial.NODDImatlab ) to extract the following NODDI maps (Figure ): voxel fraction of Gaussian anisotropic diffusion (extracellular volume fraction [FECV]), voxel fraction of non‐Gaussian anisotropic diffusion (intracellular volume fraction [FICV]), voxel fraction of isotropic Gaussian diffusion (FISO), and orientation dispersion index (ODI) maps. ..



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    noddi matlab toolbox  (MathWorks Inc)
    96
    MathWorks Inc noddi matlab toolbox
    Overview of diffusion MRI models evaluated in this study. Schematic illustration of the diffusion modeling frameworks, including <t>DTI,</t> <t>DKI,</t> SMT, <t>NODDI</t> and SMI. The diagram highlights key modeling assumptions and acquisition requirements, with DTI based on single-shell diffusion MRI and the remaining models estimated from multi-shell data. Representative voxel-wise parametric maps are shown in the lower panel to illustrate model-dependent contrast across white matter. AD , axial diffusivity; MK , mean kurtosis; AK , axial kurtosis; V ax intra-neurite volume fraction; D ax , intra-neurite axial diffusivity; ficvf , intracellular volume fraction; odi , orientation dispersion index; fiso, isotropic volume fraction; f , intra-axonal volume fraction; and , extra-axonal perpendicular and parallel diffusivities.
    Noddi Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+noddi+toolbox/Database+Toolbox/med_rxiv__64898__2026__03__15__26348428-48-29-30
    Average 96 stars, based on 1 article reviews
    noddi matlab toolbox - by Bioz Stars, 2026-10
    96/100 stars
    		  Buy from Supplier

    Image Search Results


    Overview of diffusion MRI models evaluated in this study. Schematic illustration of the diffusion modeling frameworks, including DTI, DKI, SMT, NODDI and SMI. The diagram highlights key modeling assumptions and acquisition requirements, with DTI based on single-shell diffusion MRI and the remaining models estimated from multi-shell data. Representative voxel-wise parametric maps are shown in the lower panel to illustrate model-dependent contrast across white matter. AD , axial diffusivity; MK , mean kurtosis; AK , axial kurtosis; V ax intra-neurite volume fraction; D ax , intra-neurite axial diffusivity; ficvf , intracellular volume fraction; odi , orientation dispersion index; fiso, isotropic volume fraction; f , intra-axonal volume fraction; and , extra-axonal perpendicular and parallel diffusivities.

    Journal: medRxiv

    Article Title: Comparative Evaluation of Microstructural Diffusion Methods in Characterizing Multiple Sclerosis Lesions: The Importance of multi-b shells acquisition

    doi: 10.64898/2026.03.15.26348428

    Figure Lengend Snippet: Overview of diffusion MRI models evaluated in this study. Schematic illustration of the diffusion modeling frameworks, including DTI, DKI, SMT, NODDI and SMI. The diagram highlights key modeling assumptions and acquisition requirements, with DTI based on single-shell diffusion MRI and the remaining models estimated from multi-shell data. Representative voxel-wise parametric maps are shown in the lower panel to illustrate model-dependent contrast across white matter. AD , axial diffusivity; MK , mean kurtosis; AK , axial kurtosis; V ax intra-neurite volume fraction; D ax , intra-neurite axial diffusivity; ficvf , intracellular volume fraction; odi , orientation dispersion index; fiso, isotropic volume fraction; f , intra-axonal volume fraction; and , extra-axonal perpendicular and parallel diffusivities.

    Article Snippet: In contrast, DKI, SMT, NODDI and SMI were estimated from multi-shell diffusion MRI data using a MATLAB-based DKI estimator( https://www.mathworks.com/matlabcentral/fileexchange/65487-diffusion-kurtosis-imaging-estimator ), an open-source SMT toolbox ( https://github.com/ekaden/smt ), the NODDI MATLAB toolbox ( http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab ) and the NYU Diffusion MRI Group SMI toolbox ( https://github.com/NYU-DiffusionMRI/SMI ), respectively.

    Techniques: Diffusion-based Assay, Dispersion

    Group comparisons of diffusion metrics across five white matter tissue types in MS and HC. Tissue classes included cBHs (chronic black holes), T2-lesions, cBHs-NAWM and T2-NAWM (normal-appearing white matter), and NWM (normal white matter). Diffusion metrics were derived from DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), SMT (intra-axonal signal fraction [ V ax ], extra-axonal diffusivity [ D ex ]), NODDI (intracellular volume fraction [ ficvf ], isotropic volume fraction [ fiso ], orientation dispersion index [ odi ], kappa ), and SMI (intra-axonal fraction [ f ], intra-axonal diffusivity [ D a ], extra-axonal parallel diffusivity [ ], extra-axonal perpendicular diffusivity [ ], fiber orientation coherence [ p 2 ]).

    Journal: medRxiv

    Article Title: Comparative Evaluation of Microstructural Diffusion Methods in Characterizing Multiple Sclerosis Lesions: The Importance of multi-b shells acquisition

    doi: 10.64898/2026.03.15.26348428

    Figure Lengend Snippet: Group comparisons of diffusion metrics across five white matter tissue types in MS and HC. Tissue classes included cBHs (chronic black holes), T2-lesions, cBHs-NAWM and T2-NAWM (normal-appearing white matter), and NWM (normal white matter). Diffusion metrics were derived from DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), SMT (intra-axonal signal fraction [ V ax ], extra-axonal diffusivity [ D ex ]), NODDI (intracellular volume fraction [ ficvf ], isotropic volume fraction [ fiso ], orientation dispersion index [ odi ], kappa ), and SMI (intra-axonal fraction [ f ], intra-axonal diffusivity [ D a ], extra-axonal parallel diffusivity [ ], extra-axonal perpendicular diffusivity [ ], fiber orientation coherence [ p 2 ]).

    Article Snippet: In contrast, DKI, SMT, NODDI and SMI were estimated from multi-shell diffusion MRI data using a MATLAB-based DKI estimator( https://www.mathworks.com/matlabcentral/fileexchange/65487-diffusion-kurtosis-imaging-estimator ), an open-source SMT toolbox ( https://github.com/ekaden/smt ), the NODDI MATLAB toolbox ( http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab ) and the NYU Diffusion MRI Group SMI toolbox ( https://github.com/NYU-DiffusionMRI/SMI ), respectively.

    Techniques: Diffusion-based Assay, Derivative Assay, Dispersion