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15 results for “qsm”
[2019 QSM Reconstruction Challenge] Submissions Stage 1
<p>This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
[2019 QSM Reconstruction Challenge] Submissions Stage 2
<p>This repository contains the original, unaltered files submitted to Stage 2 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
[2019 QSM Reconstruction Challenge] Metrics and Submission Information
<p>This repository contains information about submitted solutions and resulting analysis metrics of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The original susceptibility maps submitted for participation in the challenge are available <a href="http://dx.doi.org/10.5281/zenodo.3687342">here</a> and <a href="http://dx.doi.org/10.5281/zenodo.3688703">here</a>.</p> <p>The package contains seven Comma-Separated Values (CSV) files and two PDF files:</p> <ul> <li><em>master_stage1_anonymized.csv</em>: Results of stage 1 of the challenge at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr1_anonymized.csv</em>: Results of stage 2 of the challenge using the high noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>master_stage2_snr2_anonymized.csv</em>: Results of stage 2 of the challenge using the low noise dataset at the time of presentation at the workshop (fully-blinded);</li> <li><em>submission_form_stage1.pdf</em>: PDF export of the online form used in stage 1;</li> <li><em>submission_form_stage2.pdf</em>: PDF export of the online form used in stage 2.</li> </ul> <p>For the manuscript, we analyzed these CSV files with scripts reported <a href="https://doi.org/10.5281/zenodo.4559540">here</a>.</p> <p>Each csv file contains metrics for all submitted solutions along with detailed information about the algorithm used, provided by the participant at the time of submission. The very first record in each file is a header containing a list of field names:</p> <ul> <li><em>normalized rmse</em>: Whole-brain root-mean-squared error relative to ground truth;</li> <li><em>rmse_detrend_tissue</em>: Root-mean-squared error relative to ground truth (after detrending) in grey and white matter mask;</li> <li><em>rmse_detrend_blood</em>: Root-mean-squared error relative to ground truth (after detrending) using a one-pixel dilated vein mask;</li> <li><em>rmse_detrend_DGM</em>: Root-mean-squared error relative to ground truth (after detrending) in a deep gray matter mask (substantia nigra & subthalamic nucleus, red nucleus, dentate nucleus, putamen, globus pallidus and caudate);</li> <li><em>DeviationFromLinearSlope</em>: Absolute difference between the slope of the average value of the six deep gray matter regions vs. the prescribed mean value and 1.0;</li> <li><em>CalcStreak</em>: Estimation of the impact of the streaking artifact in a region of interest surrounding the calcification through the standard deviation of the difference map between reconstruction and the ground truth;</li> <li><em>DeviationFromCalcMoment</em>: Absolute deviation from the volumetric susceptibility moment of the reconstructed calcification, compared to the ground truth (computed at in the high-resolution model);</li> <li><em>Submission Identifier</em>: Self-chosen unique identifier of the submission;</li> <li><em>Submission Identifier of the corresponding Stage 1 submission</em>: This is the Submission Identifier of the solution submitted to Stage 2 that was calculated with a similar algorithm in Stage 1;</li> <li><em>Changes with respect to Stage 1 submission</em>: Self-reported information about modifications made to the algorithm for Stage 2;</li> <li><em>Number of submissions in Stage 2</em>: The number of solutions that were submitted to Stage 2 with a similar algorithm;</li> <li><em>Sim1/Sim2</em>: Filename of the submitted solutions for Stage 1;</li> <li><em>File name of the zip-file you are going to upload</em>: Filename of the file uploaded to Stage 2;</li> <li><em>Full name of the algorithm</em>: Self-reported full name of the algorithm used;</li> <li><em>Preferred Acronym</em>: Self-reported acronym of the algorithm used;</li> <li><em>Algorithm-type</em>: Self-reported type of algorithm used;</li> <li><em>Does your algorithm incorporate information derived from magnitude images?</em>: Self-reported Yes/No;</li> <li><em>Regularization terms</em>: Self-reported types of regularization terms involved;</li> <li><em>Did your algorithm use the provided frequency map or the four individual echo phase images?</em>: Self-reported information about involved magnitude information;</li> <li><em>Publication-ready description of the reconstruction technique</em>: Self-reported description of the algorithm;</li> <li><em>Publications that describe the algorithm</em>: Self-reported literature reference;</li> <li><em>Algorithm publicly available?</em>: Self-reported public availability of the algorithm;</li> <li><em>If your algorithm is not yet publicly available, would you be willing to make it available at the end of the challenge?</em>: Self-reported willingness to share the algorithm code with the public;</li> <li><em>Specific information about this solution</em>: Self-reported detailed information about the solution;</li> <li><em>Herewith, I permit the QSM Challenge committee to publish my uploaded files (calculated maps) after the completion of the challenge</em>: Self reported agreement with publication of submitted solution;</li> <li><em>Ground truth was not explicitly or implicitly incorporated into your algorithm or solution</em>: Self-reported confirmation that the ground truth was not incorporated in the solution.</li> </ul>
Point clouds and QSM for 598 individually scanned tree branches to support "Terrestrial laser scanning to reconstruct branch architecture from harvested branches"
<p>A collection of 598 harvested branches scanned in high resolution using terrestrial LiDAR. Branches were collected from GEM forest plots in Malaysia, Australia and Brazil.</p> <p>Branch nomenclature is <em>PLOT</em>-<em>TREE</em>-B<em>N</em><S or SH> where <em>PLOT </em>codes can be found in the manuscript, <em>N</em> refers to the sample number of branch harvested from a tree, and S and SH refer to sun or shade branch respectivetly.</p> <p>For each branch there are 3 files: </p> <ul> <li>Unfiltered point clouds (raw_pc) are clipped from the original data only and have had no post processing applied</li> <li>Filtered point clouds (filtered_pc) have been filtered according to the steps in the manuscript</li> <li>QSMs were produced using <em>treegraph </em>(<a href="https://doi.org/10.5281/zenodo.5226212">https://doi.org/10.5281/zenodo.5226212</a>)</li> </ul> <p>Polygon File Format (.ply) files can be viewed in software such as CloudCompare.</p> <p>Full details of methods can be found in: Wilkes, P., Shenkin, A., Disney, M., Malhi, Y., Bentley, L. P., & Vicari, M. B. (2021). Terrestrial laser scanning to reconstruct branch architecture from harvested branches. <em>Methods in Ecology and Evolution</em>, 12, 2487–2500. <a href="https://doi.org/10.1111/2041-210X.13709">https://doi.org/10.1111/2041-210X.13709</a></p>
Data from: Comparison of T2*-weighted and QSM contrasts in Parkinson's disease to visualize the STN with MRI
Open the record for dataset details and reuse information.
Post-mortem QSM and R2* maps
<p>This repository contains data associated with the following publication:</p> <p>Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis</p> <p>Authors: Chaoyue Wang, Sean Foxley, Olaf Ansorge, Sarah Bangerter-Christensen, Mark Chiew, Anna Leonte, Ricarda A.L. Menke, Jeroen Mollink, Menuka Pallebage-Gamarallage, Martin R. Turner, Karla L. Miller*, Benjamin C. Tendler* (* indicates equal contribution)</p> <p>The text file dataset_loc_QSM_R2s.txt contains a link to the dataset. Further information about the dataset can be found in dataset_info.txt and the publication.<br> </p>
Quantitative Susceptibility Mapping (QSM) to Guide Iron Chelating Therapy
ClinicalTrials.gov study NCT04171635. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessment of the Impact of Gadolinium Injection on the Measurement of the QSM Signal
ClinicalTrials.gov study NCT04906941. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of the Performance of an Optimized 3D EPI SWI Sequence and a Non-EPI QSM SWI Sequence in Detecting the Central Vein Sign in Patients With Multiple Sclerosis
ClinicalTrials.gov study NCT04705870. IPD Sharing: NO. Countries: 1. Publications: 0.
QSM and Regional DCE MRI Permeability Using GOCART Technique
ClinicalTrials.gov study NCT03091803. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of the Reproducibility of the Measurement of the QSM Signal (Quantitative Susceptibility Mapping)
ClinicalTrials.gov study NCT04465448. IPD Sharing: NO. Countries: 1. Publications: 0.
MRI QSM Imaging for Iron Overload
ClinicalTrials.gov study NCT04631718. IPD Sharing: NO. Countries: 1. Publications: 0.
Évaluation de l'Impact du Compressed Sensing Sur le Signal QSM
ClinicalTrials.gov study NCT04907487. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Longitudinal Quantitative Susceptibility Mapping (QSM) in Alzheimer 's Disease
ClinicalTrials.gov study NCT02752750. IPD Sharing: NO. Countries: 0. Publications: 0.
QSM-detected iron accumulation in the cerebellar gray matter is selectively associated with executive dysfunction in non-demented ALS patients
<p>Dataset associated with the paper "QSM-detected iron accumulation in the cerebellar gray matter is selectively associated with executive dysfunction in non-demented ALS patients". <span>These datasets cannot be made publicly available on ethical legal grounds but can be made available upon reasonable request of interested researchers to the Corresponding Author(s), who will forward a request for a data transfer agreement to the relevant Ethical Committee(s). Please, write to Dr. Barbara Poletti (b.poletti@auxologico.it).</span></p>
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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
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.