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24 results for “dMRI”
individual_dMRI_fMRI
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plp_nf1_dMRI_fMRI
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Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time"
<p>We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.</p> <p>We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500<span class="math-tex">\(\mu\)</span>m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.</p> <p>- The diffusion MRI data are contained in the files with 'dwis' in the name.<br> <br> - The corpus callosum masks are contained in the files with 'mask' in the name.</p> <p>- The acquisition parameters are contained in the .txt files.</p>
Simulated dMRI images and ground truth of random fiber phantoms in various configurations
<p>This archive contains simulated dMRI images of random fiber phantoms in various configurations created with Fiberfox and other tools available in MITK Diffusion (<a href="http://mitk.org/wiki/DiffusionImaging">http://mitk.org/wiki/DiffusionImaging</a>). RandomFibers_Example.png illustrates one of the random fiber configurations used for these phantoms.</p> <p>If you are using any of these datasets or the tools used to generate them, please don't forget to cite the dataset itself as well as other relevant publications.</p> <p>Each subfolder contains the following elements:<br> The simulated dMRI image with b-values and gradient directions: dwi.nii.gz, dwi.bvals, dwi.bvecs<br> The fibers used for simulation: AllBundles.fib (binary vtk format)<br> parameters.ffp: Fiberfox simulation parameters<br> parameters.ffp.bvals: b-value file for Fiberfox simulation<br> parameters.ffp.bvecs: gradient vector file for Fiberfox simulation<br> parameters.ffp_VOLUME1.nii.gz: fiber compartment volume fraction map for Fiberfox simulation<br> The logfile detailing all steps of the generation process of the respective phantom: LOGFILE.json</p> <p>bundles: folder containing the individual fiber bundles (binary vtk format .fib)<br> centroids: folder containing the centerlines of each bundle<br> masks: folder containing the binary envelope of each bundle<br> peaks: folder containing the principal fiber direction image (peaks) of each bundle</p> <p>Each subfolder contains the fibers and dMRI simulations with the following fiber specifications:<br> Phantom 1:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 2:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 3:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 4:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 5:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 6:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 7:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 8:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 9:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 10:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 11:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 12:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 13:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 14:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 15:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 16:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p>
dMRI data from the PRIME-DE processed by mrHARDIflow
<p>This dataset contains processed diffusion MRI data of primate subjects, DTI<sup>[1]</sup>, DIAMOND<sup>[2]</sup> and fODF<sup>[3]</sup> reconstruction and metrics. Subjects included in the dataset come from 3 sites of the PRIME-DE<sup>[4]</sup> database, specifically Aix-Marseille, UC-Davis and Mount Sinai - Philips. The data was processed using mrHARDIflow<sup>[5]</sup>, a pipeline tailored to process high resolution diffusion data. Please, cite the paper, as well as the PRIME-DE paper, if you use any data available here in your research.</p> <ol> <li>LeBihan, D., Mangin, J.-F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging 13, 534–546. doi:<a href="http://doi.org/10.1002/jmri.1076">10.1002/jmri.1076</a></li> <li>Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S. P., and Warfield, S. K. (2016). Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (diamond). Magnetic Resonance in Medicine 76, 963–977. doi:<a href="http://doi.org/10.1002/mrm.25912">10.1002/mrm.25912</a></li> <li>Tournier, J.-D., Calamante, F., and Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion mri: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472. doi:<a href="http://doi.org/10.1016/j.neuroimage.2007.02.016">10.1016/j.neuroimage.2007.02.016</a></li> <li>Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., et al. (2018). An open resource for non-human primate imaging. Neuron 100, 61–74. doi:<a href="http://doi.org/10.1016/j.neuron.2018.08.039">10.1016/j.neuron.2018.08.039</a></li> <li> <p>Valcourt Caron, A., Shmuel, A., Hao, Z., and Descoteaux, M. (2021). mrHARDIflow : A pipeline tailored for the preprocessing and analysis of Multi-Resolution High Angular diffusion MRI and its application to a variability study of the PRIME-DE database. bioRxiv. doi:<a href="http://doi.org/10.1101/2021.11.22.469616">10.1101/2021.11.22.469616 </a></p> </li> </ol> <p> </p>
Human Population-Averaged dMRI Templates (FIB Files, NIFTI Files)
<p>The templates were constructed by DSI Studio using q-space diffeomorphic reconstruction.</p> <p>Methods and Data Source: https://brain.labsolver.org/hcp_template.html</p> <p> </p> <p> </p>
INDI PRIME-DE dMRI Data (SRC Files)
<p>Source data: https://fcon_1000.projects.nitrc.org/indi/indiPRIME.html</p> <p>Shared under Creative Commons – Attribution-NonCommercial Share Alike (CC-BY-NC-SA)</p> <p>The dMRI were processed by FSL TOPUP/EDDY and DSI Studio to generate SRC Files.</p> <p> </p> <p> </p>
dMRI-RCNN Pre-Trained Weights - 3D 10in xmax
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10, with varying xmax normalisation.</p>
HCP-MGH Subject dMRI Data (SRC Files)
<p>A multishell diffusion scheme was used, and the b-values were 1000 ,3000 ,5000 and 10000 s/mm2. The number of diffusion sampling directions were 64, 64, 128, and 256. The in-plane resolution was 1.5 mm. The slice thickness was 1.5 mm.</p> <p>This data set was originally from the connectome db website (https://db.humanconnectome.org/). The MGH HCP team has released diffusion imaging and structural imaging data acquired from 35 young adults using the customized MGH Siemens 3T Connectome scanner, which has 300 mT/m maximum gradient strength for diffusion imaging.</p> <p> </p> <p>The data are shared under the WU-Minn HCP open access data use term (4) at https://www.humanconnectome.org/study/hcp-young-adult/document/wu-minn-hcp-consortium-open-access-data-use-terms</p> <p>Please acknowledge the source to the WU-Minn HCP.</p>
dMRI-RCNN Pre-Trained Weights - 1D b1000 10q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 10, b = 1000.</p>
dMRI-RCNN Pre-Trained Weights - 3D b1000 10q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10, b = 1000.</p>
dMRI-RCNN Pre-Trained Weights - 3D b3000 10q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10, b = 3000.</p>
dMRI-RCNN Pre-Trained Weights - 1D b3000 10q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 10, b = 3000.</p>
dMRI-RCNN Pre-Trained Weights - 3D b1000 30q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 30, b = 1000.</p>
dMRI-RCNN Pre-Trained Weights - 3D b1000 6q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 6, b = 1000.</p>
dMRI-RCNN Pre-Trained Weights - 1D b2000 10q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 10, b = 2000.</p>
dMRI-RCNN Pre-Trained Weights - 3D 10q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10 b = {1000, 2000, 3000}.</p>
dMRI-RCNN Pre-Trained Weights - 1D b1000 6q
<p>Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 6, b = 1000.</p>
HCP-YA 1065 Subjects dMRI Data (2mm FIB Files)
<p>The minimally-preprocessed data (Glasser et al., 2013) from Human Connectome Projects (Q1-Q4 release, 2015) were acquired by Washington University in Saint Louis and University of Minnesota (Van Essen et al., 2012). The diffusion MRI scan was conducted on a Siemens 3T Skyra scanner using a 2D spin-echo single-shot multiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse. The spatial resolution was 1.25 mm isotropic. TR = 5500 ms, TE = 89.50 ms. The b-values were 1000, 2000, and 3000 s/mm2. The total number of diffusion sampling directions was 90, 90, and 90 for each of the shells in addition to 6 b0 images. The preprocessed data were corrected for eddy current and susceptibility artifact. The accuracy of b-table orientation was examined by comparing fiber orientations with those of a population-averaged template (Yeh et al. Neuroimage, 2018). The restricted diffusion was quantified using restricted diffusion imaging (Yeh et al., MRM, 77:603–612 (2017)). The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of 1.7.</p>
Unilateral Glaucoma 3T dMRI
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