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noddi matlab toolbox  (MathWorks Inc)


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    MathWorks Inc noddi matlab toolbox
    Schematic diagram of the data processing pipeline for dMRI-based tractometry approaches. In common practice, after acquiring dMRI data (left top panel), researchers may apply denoising algorithms on the dMRI dataset. We compared the analysis results with (purple) and without denoising (yellow) while keeping the subsequent processing procedure the same. dMRI data are then typically preprocessed to correct for susceptibility- and eddy-current distortions , . After preprocessing, researchers fit voxelwise diffusion models (diffusion tensor imaging, DTI; neurite orientation <t>and</t> <t>dispersion</t> imaging, <t>NODDI)</t> to dMRI data in each voxel to quantify white matter microstructural properties. Tractography is used to identify a white matter tract of interest (in this study, the optic tract; green in the left bottom panel). Researchers can then calculate a tract profile , , which is a summary of voxelwise measurements along the tract (bottom middle figures). Finally, these tract profiles were averaged along the spatial position along the tract to obtain a single-number summary per subject, for each metric and tract. We compared these metrics per subject between data with and without denoising.
    Noddi Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/noddi matlab toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    noddi matlab toolbox - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma"

    Article Title: Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma

    Journal: Scientific Reports

    doi: 10.1038/s41598-025-10947-6

    Schematic diagram of the data processing pipeline for dMRI-based tractometry approaches. In common practice, after acquiring dMRI data (left top panel), researchers may apply denoising algorithms on the dMRI dataset. We compared the analysis results with (purple) and without denoising (yellow) while keeping the subsequent processing procedure the same. dMRI data are then typically preprocessed to correct for susceptibility- and eddy-current distortions , . After preprocessing, researchers fit voxelwise diffusion models (diffusion tensor imaging, DTI; neurite orientation and dispersion imaging, NODDI) to dMRI data in each voxel to quantify white matter microstructural properties. Tractography is used to identify a white matter tract of interest (in this study, the optic tract; green in the left bottom panel). Researchers can then calculate a tract profile , , which is a summary of voxelwise measurements along the tract (bottom middle figures). Finally, these tract profiles were averaged along the spatial position along the tract to obtain a single-number summary per subject, for each metric and tract. We compared these metrics per subject between data with and without denoising.
    Figure Legend Snippet: Schematic diagram of the data processing pipeline for dMRI-based tractometry approaches. In common practice, after acquiring dMRI data (left top panel), researchers may apply denoising algorithms on the dMRI dataset. We compared the analysis results with (purple) and without denoising (yellow) while keeping the subsequent processing procedure the same. dMRI data are then typically preprocessed to correct for susceptibility- and eddy-current distortions , . After preprocessing, researchers fit voxelwise diffusion models (diffusion tensor imaging, DTI; neurite orientation and dispersion imaging, NODDI) to dMRI data in each voxel to quantify white matter microstructural properties. Tractography is used to identify a white matter tract of interest (in this study, the optic tract; green in the left bottom panel). Researchers can then calculate a tract profile , , which is a summary of voxelwise measurements along the tract (bottom middle figures). Finally, these tract profiles were averaged along the spatial position along the tract to obtain a single-number summary per subject, for each metric and tract. We compared these metrics per subject between data with and without denoising.

    Techniques Used: Diffusion-based Assay, Imaging, Dispersion

    Comparison of voxelwise model fitting in the OT between dMRI data with and without denoising. ( A ) Fitting of the diffusion tensor model (diffusion tensor imaging, DTI). The vertical axis represents the fitting error of the DTI quantified by the root mean square error (RMSE) for dMRI data with and without denoising (MPPCA and Patch2Self) in the OT, where a lower RMSE corresponds to a smaller error. Open squares/circles depict the data of individual subjects (blue square, controls; red circle, patients with glaucoma). Data points connected among different conditions (without denoising, with MPPCA, and with Patch2Self) by lines are data acquired from identical subjects. Thick horizontal lines in the violin plot represent the mean across subjects, whereas the widths of the violin plot represent the approximate frequency of data points in each condition and RMSE. ( B ) Fitting of the neurite orientation dispersion and density imaging (NODDI). The vertical axis depicts the fitting error of the NODDI quantified by the Rician log-likelihood for dMRI data with and without denoising in the OT. A higher Rician log-likelihood indicates smaller error. The other conventions are the same as those used in panel A.
    Figure Legend Snippet: Comparison of voxelwise model fitting in the OT between dMRI data with and without denoising. ( A ) Fitting of the diffusion tensor model (diffusion tensor imaging, DTI). The vertical axis represents the fitting error of the DTI quantified by the root mean square error (RMSE) for dMRI data with and without denoising (MPPCA and Patch2Self) in the OT, where a lower RMSE corresponds to a smaller error. Open squares/circles depict the data of individual subjects (blue square, controls; red circle, patients with glaucoma). Data points connected among different conditions (without denoising, with MPPCA, and with Patch2Self) by lines are data acquired from identical subjects. Thick horizontal lines in the violin plot represent the mean across subjects, whereas the widths of the violin plot represent the approximate frequency of data points in each condition and RMSE. ( B ) Fitting of the neurite orientation dispersion and density imaging (NODDI). The vertical axis depicts the fitting error of the NODDI quantified by the Rician log-likelihood for dMRI data with and without denoising in the OT. A higher Rician log-likelihood indicates smaller error. The other conventions are the same as those used in panel A.

    Techniques Used: Comparison, Diffusion-based Assay, Imaging, Dispersion



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    noddi matlab toolbox  (MathWorks Inc)
    90
    MathWorks Inc noddi matlab toolbox
    Schematic diagram of the data processing pipeline for dMRI-based tractometry approaches. In common practice, after acquiring dMRI data (left top panel), researchers may apply denoising algorithms on the dMRI dataset. We compared the analysis results with (purple) and without denoising (yellow) while keeping the subsequent processing procedure the same. dMRI data are then typically preprocessed to correct for susceptibility- and eddy-current distortions , . After preprocessing, researchers fit voxelwise diffusion models (diffusion tensor imaging, DTI; neurite orientation <t>and</t> <t>dispersion</t> imaging, <t>NODDI)</t> to dMRI data in each voxel to quantify white matter microstructural properties. Tractography is used to identify a white matter tract of interest (in this study, the optic tract; green in the left bottom panel). Researchers can then calculate a tract profile , , which is a summary of voxelwise measurements along the tract (bottom middle figures). Finally, these tract profiles were averaged along the spatial position along the tract to obtain a single-number summary per subject, for each metric and tract. We compared these metrics per subject between data with and without denoising.
    Noddi Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/noddi matlab toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    noddi matlab toolbox - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Schematic diagram of the data processing pipeline for dMRI-based tractometry approaches. In common practice, after acquiring dMRI data (left top panel), researchers may apply denoising algorithms on the dMRI dataset. We compared the analysis results with (purple) and without denoising (yellow) while keeping the subsequent processing procedure the same. dMRI data are then typically preprocessed to correct for susceptibility- and eddy-current distortions , . After preprocessing, researchers fit voxelwise diffusion models (diffusion tensor imaging, DTI; neurite orientation and dispersion imaging, NODDI) to dMRI data in each voxel to quantify white matter microstructural properties. Tractography is used to identify a white matter tract of interest (in this study, the optic tract; green in the left bottom panel). Researchers can then calculate a tract profile , , which is a summary of voxelwise measurements along the tract (bottom middle figures). Finally, these tract profiles were averaged along the spatial position along the tract to obtain a single-number summary per subject, for each metric and tract. We compared these metrics per subject between data with and without denoising.

    Journal: Scientific Reports

    Article Title: Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma

    doi: 10.1038/s41598-025-10947-6

    Figure Lengend Snippet: Schematic diagram of the data processing pipeline for dMRI-based tractometry approaches. In common practice, after acquiring dMRI data (left top panel), researchers may apply denoising algorithms on the dMRI dataset. We compared the analysis results with (purple) and without denoising (yellow) while keeping the subsequent processing procedure the same. dMRI data are then typically preprocessed to correct for susceptibility- and eddy-current distortions , . After preprocessing, researchers fit voxelwise diffusion models (diffusion tensor imaging, DTI; neurite orientation and dispersion imaging, NODDI) to dMRI data in each voxel to quantify white matter microstructural properties. Tractography is used to identify a white matter tract of interest (in this study, the optic tract; green in the left bottom panel). Researchers can then calculate a tract profile , , which is a summary of voxelwise measurements along the tract (bottom middle figures). Finally, these tract profiles were averaged along the spatial position along the tract to obtain a single-number summary per subject, for each metric and tract. We compared these metrics per subject between data with and without denoising.

    Article Snippet: We also fitted NODDI to the dMRI data to estimate the intracellular volume fraction (ICVF) and orientation dispersion index (ODI) using the NODDI MATLAB toolbox ( http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab ).

    Techniques: Diffusion-based Assay, Imaging, Dispersion

    Comparison of voxelwise model fitting in the OT between dMRI data with and without denoising. ( A ) Fitting of the diffusion tensor model (diffusion tensor imaging, DTI). The vertical axis represents the fitting error of the DTI quantified by the root mean square error (RMSE) for dMRI data with and without denoising (MPPCA and Patch2Self) in the OT, where a lower RMSE corresponds to a smaller error. Open squares/circles depict the data of individual subjects (blue square, controls; red circle, patients with glaucoma). Data points connected among different conditions (without denoising, with MPPCA, and with Patch2Self) by lines are data acquired from identical subjects. Thick horizontal lines in the violin plot represent the mean across subjects, whereas the widths of the violin plot represent the approximate frequency of data points in each condition and RMSE. ( B ) Fitting of the neurite orientation dispersion and density imaging (NODDI). The vertical axis depicts the fitting error of the NODDI quantified by the Rician log-likelihood for dMRI data with and without denoising in the OT. A higher Rician log-likelihood indicates smaller error. The other conventions are the same as those used in panel A.

    Journal: Scientific Reports

    Article Title: Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma

    doi: 10.1038/s41598-025-10947-6

    Figure Lengend Snippet: Comparison of voxelwise model fitting in the OT between dMRI data with and without denoising. ( A ) Fitting of the diffusion tensor model (diffusion tensor imaging, DTI). The vertical axis represents the fitting error of the DTI quantified by the root mean square error (RMSE) for dMRI data with and without denoising (MPPCA and Patch2Self) in the OT, where a lower RMSE corresponds to a smaller error. Open squares/circles depict the data of individual subjects (blue square, controls; red circle, patients with glaucoma). Data points connected among different conditions (without denoising, with MPPCA, and with Patch2Self) by lines are data acquired from identical subjects. Thick horizontal lines in the violin plot represent the mean across subjects, whereas the widths of the violin plot represent the approximate frequency of data points in each condition and RMSE. ( B ) Fitting of the neurite orientation dispersion and density imaging (NODDI). The vertical axis depicts the fitting error of the NODDI quantified by the Rician log-likelihood for dMRI data with and without denoising in the OT. A higher Rician log-likelihood indicates smaller error. The other conventions are the same as those used in panel A.

    Article Snippet: We also fitted NODDI to the dMRI data to estimate the intracellular volume fraction (ICVF) and orientation dispersion index (ODI) using the NODDI MATLAB toolbox ( http://mig.cs.ucl.ac.uk/index.php?n=Tutorial.NODDImatlab ).

    Techniques: Comparison, Diffusion-based Assay, Imaging, Dispersion