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153 results for “Diffusion MRI”
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
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24-shell diffusion MRI data
<p>24-shell diffusion MRI used in <em>Huynh, K.M., Xu, T., Wu, Y., Wang, X., Chen, G., Wu, H., Thung, K.H., Lin, W., Shen, D. and Yap, P.T., 2020. Probing tissue microarchitecture of the baby brain via spherical mean spectrum imaging. IEEE Transactions on Medical Imaging, 39(11), pp.3607-3618.</em></p>
Datasets for 'Automated characterization of noise distributions in diffusion MRI data'
<p>Datasets we used for the manuscript 'Automated characterization of noise distributions in diffusion MRI data'.</p>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI : an application to the ventriclar junctions
<p>Data and materials regarding the submission of the paper. See Data Avaibility section and https://github.com/valeryozenne/Cardiac-Structure-Database</p> <p> </p>
Diffusion MRI/ PET Simulation
<p>These data are designed to simulate diffusion-weighted images using Fibrefox, analytical PET simulation with ASIM, and PET image reconstruction with STIR.</p> <p> </p> <p>file description </p> <p>/MRI<br> Fibers.fib: the ground-truth fiber tracks file from Tractography Challenge ISMRM 2015 Data<br> bvals, bvecs: the encoding scheme files for DWI simulation (similar to HCP protocol)<br> t1.mif: the fabricated T1 image<br> dwi.mif: the example DWI file simulated</p> <p>/PET<br> AAL_ROI_upsample: a template phantom file with 3 lesions <br> asim_pipeline.sh: batch code for ASIM simulation<br> AAL_ROI_noisy_2: normalised PET sinogram 1e+08 total count, ready to be reconstructed<br> AAL_ROI_noisy.yaff: normalised PET sinogram 1e+07 total count, ready to be reconstructed<br> AAL_ROI_noisy_2.yhdr: ASIM header file<br> OSEM_m962,par hdr_E_m962.hs: header files for STIR reconstruction with OSMAPOSL</p>
Data from: Direct segmentation of cortical cytoarchitectonic domains using ultra-high-resolution whole-brain diffusion MRI
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Noninvasive quantification of axon radii using diffusion MRI
<p>Axon size plays a crucial role in determining conductance velocity and, consequently, in the the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. However, until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how -- when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated -- heavily diffusion-weighted MRI signals becomes sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric representing the entire axon radius distribution within a voxel that emphasizes the largest axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified.</p>
Ultra-high-resolution diffusion MRI atlas of CD1 embryonic mouse brains at E10.5-E15.5.
<p>This dataset is assciated with the paper "A Spatiotemporal Continuum of Embryonic Mouse Brain Development Built on Diffusion MR Microscopy for Probing Dynamic Gene-Neuroanatomy" on PNAS. For each stage, average FA (fractional anisotropy) and DEC (directionally encoded colormap) images (n=5) are provided. <br> <br> </p>
Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
<p>This whole-brain in vivo diffusion MRI dataset was acquired at 760 µm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy subject. It was acquired using state-of-the-art acquisition hardware and advanced reconstruction to achieve high SNR at such resolution, including a high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition, and parallel imaging reconstruction with advanced ghost reduction algorithms. With its unprecedented high resolution, SNR and image quality, it could help explore the fine-scale structures of in vivo human brain, and further advance the understanding of human brain connectivity. This dataset can also be used as a test bed for further technical development of new modeling, sub-sampling strategies, denoising and processing algorithms for in vivo high resolution dMRI. Whole brain anatomical T<sub>1</sub>-weighted and T<sub>2</sub>-weighted images at submillimeter scale, field maps and the code for preprocessing pipeline are also made available in the repository.</p>
Diffusion MRI quality control reports for the BTC datasets
<p>Reports (HTML and pdf) of quality control on diffusion data on brain tumor patients before and after surgery, and controls, published at</p> <p>https://openneuro.org/datasets/ds001226 and https://openneuro.org/datasets/ds002080,</p> <p>based on Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019 Jan 1;184:801-812. doi: 10.1016/j.neuroimage.2018.09.073. Epub 2018 Sep 26. PMID: 30267859; PMCID: PMC6264528.</p>
Diffusion-relaxation MRI data from biomimetic phantoms
<p>Diffusion-relaxation MRI data and tools to estimate the inner fibre radius of biomimetic phantoms, as reported here:</p> <blockquote> <p><strong>Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI</strong>. Erick J. Canales-Rodríguez, Marco Pizzolato, Feng-Lei Zhou, Muhamed Barakovic, Jean-Philippe Thiran, Derek K. Jones, Geoffrey J.M. Parker, Tim B. Dyrby. Magn Reson Med. 2024; 91: 2579-2596. doi: 10.1002/mrm.29991 <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29991" rel="nofollow">https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29991</a></p> </blockquote> <p>Run the code to replicate the figures reported in the paper. We provide the raw diffusion-relaxation data (spherical mean signal) to facilitate future evaluations and developments.</p> <p> </p> <p> </p>
Ex vivo 100 μm isotropic diffusion MRI‐based tractography of connectivity changes in the end‐stage R6/2 mouse model of Huntington's disease
<div> <div> <div> <div> <p><strong>Background</strong>: Huntington's disease is a progressive neurodegenerative disorder. Brain atrophy, as measured by volumetric magnetic resonance imaging (MRI), is a downstream consequence of neurodegeneration, but microstructural changes within brain tissue are expected to precede this volumetric decline. The tissue microstructure can be assayed non-invasively using diffusion MRI, which also allows a tractographic analysis of brain connectivity.</p> <p><strong>Methods</strong>: We here used ex vivo diffusion MRI (11.7T) to measure microstructural changes in different brain regions of end‐stage (14 weeks of age) wild type and R6/2 mice (male and female) modeling Huntington's disease. To probe the microstructure of different brain regions, reduce partial volume effects and measure connectivity between different regions, a 100 μm isotropic voxel resolution was acquired.</p> <p><strong>Results</strong>: Although fractional anisotropy did not reveal any difference between wild‐type controls and R6/2 mice, mean, axial, and radial diffusivity were increased in female R6/2 mice and decreased in male R6/2 mice. Whole brain streamlines were only reduced in male R6/2 mice, but streamline density was increased. Region‐to‐region tractography indicated reductions in connectivity between the cortex, hippocampus, and thalamus with the striatum, as well as within the basal ganglia (striatum—globus pallidus—subthalamic nucleus—substantia nigra—thalamus).</p> <p><strong>Conclusions</strong>: Biological sex and left/right hemisphere affected tractographic results, potentially reflecting different stages of disease progression. This proof‐of‐principle study indicates that diffusion MRI and tractography potentially provide novel biomarkers that connect volumetric changes across different brain regions. In a translation setting, these measurements constitute a novel tool to assess the therapeutic impact of interventions such as neuroprotective agents in transgenic models, as well as patients with Huntington's disease.</p> </div> </div> </div> </div>
Data-driven characterization and correction of the orientation dependence of magnetization transfer measures using diffusion MRI
<p>These are the produced plots for every subjects and every sessions of the study. Here are the characterization plots, the corrected plots, and the tractometry plots.</p>
MRI Diffusion Tensor Tractography to Monitor Peripheral Nerve Recovery After Severe Crush or Cut/Repair Nerve Injury
ClinicalTrials.gov study NCT02960516. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Evaluation of Cerebral Parenchymal Changes in Patients Undergoing Proximal Aortic Surgery With Deep Hypothermic Circulatory Arrest Using Diffusion MRI
ClinicalTrials.gov study NCT04755439. IPD Sharing: YES. Countries: 1. Publications: 0.
Noninvasive quantification of axon radii using diffusion MRI
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Ex vivo 100 μm isotropic diffusion MRI‐based tractography of connectivity changes in the end‐stage R6/2 mouse model of Huntington's disease
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Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.