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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 2335 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/noddi matlab toolbox/product/MathWorks Inc
    Average 96 stars, based on 2335 article reviews
    noddi matlab toolbox - by Bioz Stars, 2026-04
    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



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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