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14 results for “BigBrain”
BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution
<p><strong>BigBrain-MR</strong> is a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI contrasts and properties that affect image generation. This phantom was generated from the publicly available <a href="https://bigbrainproject.org/">BigBrain histological dataset</a> and from lower-resolution in-vivo 7T-MRI data, using a new image processing framework that allows mapping the general properties of in-vivo data into the fine anatomical scale of BigBrain.</p> <p>The <strong>dataset</strong> includes:</p> <ul> <li>BigBrain original contrast and a new atlas with 20 ROIs;</li> <li>T<sub>1</sub>-weighted image and T<sub>1</sub> map;</li> <li>T<sub>2</sub>*-weighted images and R<sub>2</sub>* map;</li> <li>Magnetic susceptibility map (QSM);</li> <li>Background magnetic field map;</li> <li>Complex coil sensitivity maps (32ch-receive RF array);</li> <li>Bias field map.</li> </ul> <p>Information about each image/map (including data type and amplitude scaling) is provided in <em>data_info.txt</em>.</p> <p>Additionally, we have included a script with <strong>usage examples</strong> in Python that illustrate how the data can be loaded, processed and combined for diverse simulation purposes.</p> <p>BigBrain-MR is presented, described and tested in the following <strong>peer-reviewed article</strong>:</p> <p>C. Sainz Martinez, M. Bach Cuadra, J. Jorge. <em>BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development</em>. NeuroImage 2023. <strong>DOI:</strong> <a href="https://doi.org/10.1016/j.neuroimage.2023.120074">10.1016/j.neuroimage.2023.120074</a></p> <p> </p>
High-Resolution Heterogeneous Digital PET [18F]FDG Brain Phantom based on the BigBrain Atlas
<p>We present the design of a digital phantom that tries to overcome the problems of the current PET digital brain phantoms, particularly for the simulation of simultaneous PET-MRI data sets. We propose a new brain digital brain phantom based on the BigBrain atlas, a free, publicly available tool that provides considerable neuroanatomical insight into the human brain with an ultrahigh-resolution 3D model of a human brain at nearly cellular resolution of 20 micrometers. We used the histology maps, the classified tissue maps and the MRI image of the BigBrain atlas, as well as the Hammersmith atlas and a PET [18F]FDG template as inputs to create an instance of this ultra high-resolution heterogeneous PET-MRI phantom.</p> <p>Full details of this phantom in Medical Physics: "Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas", <a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218.</a></p> <p>You can find codes examples for reading the data at https://github.com/mabelzunce/PETBrainPhantoms </p> <p>Please cite this paper if you use this phantom in your work:</p> <p>Belzunce, M.A. and Reader, A.J. (2020), Technical Note: Ultra high‐resolution radiotracer‐specific digital pet brain phantoms based on the BigBrain atlas. Med. Phys., 47: 3356-3362. doi:<a href="https://doi.org/10.1002/mp.14218">10.1002/mp.14218</a></p>
Figure 7 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 7 - Timeline. We will prepare the gold standard evaluation data (Aim 1) and develop and update the game (Aim 2) through the 18th month as we get feedback on its use. Year 2 will consist primarily of testing the aggregation of segmented data (Aim 3), exploring how well the game can generalize to segmentation of every region of the BigBrain (Exploratory Aim 1), and training and testing an automated approach that learns from the crowdsourced data (Exploratory Aim 2), as well as to publish and present our findings.
Figure 6 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 6 - Example joint fusion of multiple label assignments. This macroscopic brain atlas was built from 20 individually labeled atlases, using joint fusion (Wang and Yushkevich 2013), after nonlinearly registering the 20 to a template (http://mindboggle.info/data.html).
Figure 2 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 2 - Example hippocampal subfield labels. Left: Example of hippocampal subfield labeling. Right: The first work demonstrating hippocampal subfield labels in MRI space that are derived from ground-truth histological imaging (Adler et al. 2014).
Figure 5 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 5 - Example boundary estimates in the BigBrain hippocampus. This figure shows example color line drawings atop a (low-resolution) image tile containing a small portion of the BigBrain's right hippocampus. The lines represent a novice's estimates of the CA1/subiculum cytoarchitectonic boundary. This boundary is extremely difficult, as it results in the greatest overall disagreement among hippocampal subfield labeling protocols (Yushkevich et al. 2015).
Figure 4b from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 4b - Analytics screenshot
Figure 3e from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3e - Eyewire neural reconstruction with scoring leaderboard
Figure 3d from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3d - EyeWire tutorial
Figure 1 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 1 - From Amunts et al. (2013)
Figure 3c from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3c - EteRNA tutorial
Figure 3b from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3b - Nanocrafter tutorial
Figure 3a from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 3a - FoldIt tutorial
Figure 4a from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 4a - Screenshot
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.