Journal: Scientific Data

Article Title: Data for evaluation of fast kurtosis strategies, b-value optimization and exploration of diffusion MRI contrast

doi: 10.1038/sdata.2016.72

Figure Lengend Snippet: Overview of the data contained in Data Citation 1.

Article Snippet: Physical phantom , Diffusion MRI at 9.4 TStructural scan , Data is provided in nifti format with b-values and difusion gradient vectors in text format. The data is also provided in the matlab.mat file format: phantom_data.matThis matlab file contains raw dMRI data (phantom_data), corresponding b-values (effective_b), gradient table (DWI_encoding_directions), and structural scan (structural_data). , Phantom_data.zip.

Techniques: Diffusion-based Assay, Imaging

Journal: Scientific Data

Article Title: Data for evaluation of fast kurtosis strategies, b-value optimization and exploration of diffusion MRI contrast

doi: 10.1038/sdata.2016.72

Figure Lengend Snippet: Overview of the data contained in Data Citation 1.

Article Snippet: Fixed rat brain , Diffusion MRI at 9.4 T , Data is provided in nifti format with b-values and difusion gradient vectors in text format. The data is also provided in the matlab.mat file: rat_brain.matThis matlab file contains raw dMRI data (rat_brain_data), corresponding b-values (nominal_bvalues and effective_b, the latter includes b-value contributions from imaging gradients) and gradient table (DWI_encoding_directions_pr_b_value). , Rat_brain.zip.

Techniques: Diffusion-based Assay, Imaging

Journal: Annals of Biomedical Engineering

Article Title: Integrating Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging to Improve the Predictive Capabilities of CED Models

doi: 10.1007/s10439-020-02598-7

Figure Lengend Snippet: (a) This picture offers a schematic representation of the leading idea behind this study which combines both DTI and NODDI imaging modalities. DTI provides information about the WM fibres directionality: on the left, an axial section of FA map of a healthy subject, displayed as colour-orientation map. Latero-lateral-oriented fibres are coded in red, cranio-caudal fibres in blue, and antero-posterior fibres in green. The neural fibres orientation (red box) is used to define the permeability tensor eigenvectors whereas NODDI, providing an insight into the axonal microstructure (black box), allows deriving the permeability tensor eigenvalues. To do so, the WM is modelled as a triangular arrangement of fibres where each grey circle represents the section of an axon and the green box is the representative volume element (RVE) analysed. (b) Model geometries used to compute \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{\parallel }$$\end{document} k ‖ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k_{ \bot }$$\end{document} k ⊥ . The green shapes represent the extracellular space of each geometry, namely, the space where the fluid can flow, which has been measured being in tens of nanometres. On the left, 3D geometry used to simulate a flow parallel to the fibres with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 0.15 \mu {\text{m}}$$\end{document} L = 0.15 μ m . On the right, the bi-dimensional geometry used to simulate a flow perpendicular to the direction of the fibres with L that varied according to different values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{VF}}_{\text{ECS}}$$\end{document} VF ECS .

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.

Techniques: Imaging, Permeability

Journal: Annals of Biomedical Engineering

Article Title: Integrating Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging to Improve the Predictive Capabilities of CED Models

doi: 10.1007/s10439-020-02598-7

Figure Lengend Snippet: Predicted GD concentration after infusion in a WM region of the brain. Top: schematic drawing representing the catheter and the section plane corresponding to the contours below. Middle: GD concentration contours obtained with the DTI and the DTI-NODDI models at 180 s. The double headed arrows represent the resulting permeability vectors on different voxels. They were obtained by summing the parallel and perpendicular components of the permeability tensor and then projecting the resulting vector on the relevant plane. Bottom: Comparison between the DTI model (red) and the DTI-NODDI model (blue) in terms of GD distribution outlines defined as the more external elements with a GD concentration higher than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c_{ \hbox{min} }$$\end{document} c min .

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.

Techniques: Concentration Assay, Permeability, Plasmid Preparation