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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 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/noddi+toolbox/Database+Toolbox/med_rxiv__64898__2026__03__15__26348428-48-29-30
    Average 96 stars, based on 2340 article reviews
    noddi matlab toolbox - by Bioz Stars, 2026-09
    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

    Derivative Assay:

    Article Title: Application of neurite orientation dispersion and density imaging in characterizing brain microstructural changes in classical trigeminal neuralgia and a comparison between the left and right sides.
    Article Snippet: Diffusion tensor imaging can detect brain white matter changes in classical trigeminal neuralgia (TN).. However, it lacks specificity for individual tissue microstructural features, such as neurite density, orientation dispersions, and extracellular edema.. Neurite orientation dispersion and density imaging (NODDI), a novel diffusion magnetic resonance imaging (MRI) technique, can provide these distinct indices.

    Article Title: Examining relationships among NODDI indices of white matter structure in prefrontal cortical-thalamic-striatal circuitry and OCD symptomatology
    Article Snippet: .. NODDI maps (NDI and ODI) were derived for each participant in native space using the NODDI toolbox, implemented in Matlab ( https://www.nitrc.org/projects/noddi_toolbox ) [ , ], and were then registered to MNI space. ..

    other:

    Article Title: Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress
    Article Snippet: NODDI matlab).22

    Dispersion:

    Article Title: Multi-TE Diffusion MRI Dataset for Exploring Combined Diffusion-Relaxometry Methods in Microstructure Imaging.
    Article Snippet: .. The NODDI model, a more complex representation of tissue microstructure that requires data from both low and high b-values with multiple gradient directions, which provides three metrics such as neurite density index (NDI), orientation dispersion index (ODI), and free water (FW), was fitted to the dataset using the NODDI toolbox executed in MATLAB 2018a (MathWorks, Natick, MA, USA) with default settings for each TE session separately. ..

    Diffusion-based Assay:

    Article Title: Disease Progression and Multiparametric Imaging Characteristics of Spinocerebellar Ataxia Type 3 With Spastic Paraplegia
    Article Snippet: .. Volume fraction of isotropic (Viso) diffusion is able to quantify the fractional volume occupied by extracellular fluid., Therefore, the NODDI model was fitted to calculate the NODDI-related metrics, including NDI, ODI, and Viso, using the NODDI toolbox based on Matlab (MathWorks, Natick, MA). ..



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    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.
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    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.
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    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.
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    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