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73 results for “MRI dataset”
Ultrasound dataset for head model speed-of-sound derived from segmented MRI (MIDA model)
Open the record for dataset details and reuse information.
Segmented LGE-MRI dataset
<p>Segmented dataset own data from Moscow Regional Scientific Research Clinical Institute. MF Vladimirsky and from cDEMRIS dataset (https://figshare.com/articles/dataset/CDEMRIS_fibrosis_scar_challenge_data_2012/4214532/5)</p>
Dataset for the project "Corticocortical signal transmission and brain connectivity alterations at prodromal stage and during the progression of Alzheimer's disease: A multimodal approach of TMS-EEG and advanced MRI" (GR-2016-02364132)
<p><strong>Aim 1:</strong></p> <p><u>Validation of STT as measure of WM integrity and as diagnostic marker of AD</u>. The first step to test the potential of the STT as AD marker is to establish if the SST can differentiate between patients (LOAD, aMCI) and healthy individuals and its relationship with clinical symptoms and with well-established measures of connectivity.</p> <p> <strong>Aim 2:</strong></p> <p><u>Connectivity changes, evaluated as in aim 1, as prognostic marker of disease progression</u>. Another crucial point is to establish if STT can capture brain connectivity alterations with disease progression, if they parallel WM integrity alterations and cognitive decline. Moreover, the potential contribution of STT as prognostic marker can be established by investigating which measure, or combination of measures, of brain connectivity at the baseline best predicts the conversion to AD in aMCI cases.</p> <p> <strong>Aim3:</strong></p> <p><u>Connectivity changes in EOAD patients.</u> The comparison of connectivity alterations between EOAD and LOAD at the baseline and during the disease progression will be a crucial step to establish the efficacy of STT as a marker of differential diagnosis in atypical forms of AD.</p> <p> </p> <p><strong>The present dataset contains the following files and folders:</strong></p> <p> </p> <p>File: <strong>Demographics_allparticipants.xlsx</strong> contains demographic and clinical data of the entire sample</p> <p>File: <strong>Hippocampal volume.xlsx</strong> data used to extract hippocampal atrophy as AD biomarker</p> <p> </p> <p>Folder: <strong>Aim1</strong></p> <p>- <strong>READ ME</strong> AIM1.txt explains folder structure</p> <p>- <strong>TMS-EEG</strong> data of HC, MCI and LOAD at T0 are organized in 3 files with TMS-Evoked Potentials (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file)</p> <p>- <strong>Neuropsychological</strong> data of HC, MCI and LOAD at T0 (.xlsx format)</p> <p>- <strong>fMRI</strong> folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T0</p> <p>- <strong>DTI</strong> data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0</p> <p> </p> <p>- Folder: <strong>Aim2</strong></p> <p>- <strong>READ ME</strong> AIM2.txt explains folder structure</p> <p>- <strong>TMS-EEG</strong> data are organized in one file that includes TMS-Evoked Potentials of HC, MCI and LOAD at T0 and T1 (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file)</p> <p>- <strong>Neuropsychological</strong> data of HC, MCI and LOAD at T1 (.xlsx format)</p> <p>- <strong>fMRI</strong> folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T1</p> <p>- <strong>DTI</strong> data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0 and T1</p> <p> </p> <p>Folder: <strong>Aim3</strong></p> <p> <strong>READ ME</strong> AIM2.txt explains folder structure</p> <p>- <strong>TMS-EEG</strong> data are organized in two files that includes TMS-Evoked Potentials of EOAD and LOAD at T0 and T1 (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file)</p> <p>- <strong>Neuropsychological</strong> data of EOAD and LOAD at T0 and T1 (.xlsx format)</p> <p>- <strong>fMRI</strong> folder contains coordinates of the hotspot for the stimulated functional networks of EOAD and LOAD at T0 and T1</p> <p>- <strong>DTI</strong> data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of EOAD and LOAD at T0 and T1</p> <p> </p> <p>Restricted Access Conditions: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.</p>
Insight MS Longitudinal MRI dataset
<p>Multiple Sclerosis (MS) is an idiopathic chronic inflammatory demyelinating disease of the central nervous system. Timely and accurate diagnosis via the McDonald criteria improves outcome but relies heavily on radiologist interpretation of MRI studies. A growing body of research is focused on identifying new MS lesions on MRI by comparing one time point with another but only limited longitudinal data are available.</p> <p>We present a dataset of which contains 496 scans of 172 patients with MS each with at least 2 time points including T1, Flair and T2 sequences. There are 110 stable scans and 214 instances of change. We also include demographic information. Data has been anonymised, processed and segmented with three stages of expert opinion contribute to ground truth.</p> <p>Future research using this dataset could include new lesion identification, radiomic characterisation of new lesions, relationship with disease activity and brain atrophy and time series applications including prediction of new lesions and active learning for efficient segmentation.</p>
Longitudinal multimodal MRI dataset of experimental ischemic stroke in male and female rats
<p>Preclinical MRI dataset (rat) accompanying https://doi.org/10.1177/0271678X231208993</p>
Dataset related to article "Quantitative Muscle MRI Protocol as Possible Biomarker in Becker Muscular Dystrophy"
<p>The database contains descriptive tables with clinical scores and quantitative MRI parameters values extracted from the thigh and the calf of the subjects involve in the study. A comparison table with the statistical correlation is also reported.</p>
Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "
<p>This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era"</p> <p>Abstract</p> <p><strong>Introduction: </strong> To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).</p> <p><strong>Materials and methods: </strong> After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.</p> <p><strong>Results: </strong> A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (<em>p</em> = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.</p> <p><strong>Conclusion: </strong> The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.</p>
Duke Liver Dataset (MRI) v2
<p>The Duke Liver DataSet (DLDS) provides over 2000 anonymized MRI image series acquired in routine liver MRI protocols across 105 subjects that can be used to train algorithms for two applications:</p> <p>(1) Series identification<br> (2) Liver segmentation</p> <p>The series identification grouping (Series_Classification.zip) includes 2146 unique image series across all 105 subjects. The image series encompass seventeen different contrast types including different pulse sequences, contrast bolus phases, and imaging orientations. A randomized, alphabetical series label corresponding to one of these seventeen contrast types is assigned to each contrast type. The file SeriesClassificationKey.csv includes a list of all image series included in the dataset and their corresponding label. The file SequenceTypes.csv shows the mapping of these labels to contrast type. It is intended that these labels can be used to train an automated series classification model for abdominal MRI. </p> <p>The liver segmentation sub-grouping (Segmentation.zip) includes 310 unique image series across a subset of 95 subjects. The image series encompass four main contrast types: axial in-phase, axial opposed, axial precontrast fat-suppressed T1w, and contrast-enhanced portal venous t1w. It is intended that this data can be used to train an automated liver segmentation model. The file SegmentationKey.csv includes a list of the cases with included masks files. Since there is overlap in these series with the series identification dataset, corresponding series labels are also included.</p> <p>In both datasets, cases are organized in directories by DLDS ID -> Series number. The segmentation cases are further divided into image and mask sub-directories.</p> <p>Please note that a previous version of this dataset (version 1.0.0) withheld roughly 25% of series labels and segmentations. These have been restored to the dataset for completeness. As reference for users wishing to know which data was added (or looking for a list of subjects to remove for a convenient test set), labels and masks were previously withheld for the following DLDS id's: [0002, 0003, 0004, 0017, 0020, 0023, 0031, 0032, 0033, 0035, 0038, 0042, 0046, 0050, 0063, 0074, 0075, 0078, 0083, 0084, 0096, 0097, 0098, 0100, 0103, 0104].</p>
Intrasession, Intersession, and Interscanner Qualitative and Quantitative MRI Datasets of Healthy Brains at 3.0T
<p>The included data are NIFTI-format images (MRRAGE, TSE, and 3D-MRF) for ten healthy volunteers collected across two 3.0 Tesla MRI scanners across three repetitions, for the purpose of assessing repeatability and reproducibility of MRF quantification versus gold-standard qualitiative imaging techniques.</p>
Dataset related to article "Role of NODDI in the MRI Characterization of Hippocampal Abnormalities in Temporal Lobe Epilepsy"
<p>The database contains data analysis tables and hippocampi histological images.</p>
Hyperpolarized MRI RTHawk Example Datasets
<p>Example datasets from Hyperpolarized metabolic MRI studies, acquired using an autonomous scanning protocol with the RTHawk Research System</p> <p>See accompanying papers for descriptions of RTHawk-based sequences used in this data:</p> <p>https://www.ncbi.nlm.nih.gov/pubmed/30277268 or https://arxiv.org/pdf/2008.08794</p> <p>https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28204 or https://arxiv.org/pdf/2008.08800</p> <p> </p>
Dataset related to article: "Single-stapled anastomosis is associated with a lower anastomotic leak rate than double-stapled technique after minimally invasive total mesorectal excision for MRI-defined low rectal cancer"
<p>This record contains raw data related to article: "<strong>Single-stapled anastomosis is associated with a lower anastomotic leak rate than double-stapled technique after minimally invasive total mesorectal excision for MRI-defined low rectal cancer"</strong></p><p><strong>Background: </strong>After total mesorectal excision, distal rectal transection and anastomosis are critical for short-term, oncological, and functional outcomes, including anastomotic leak. A double-pursestring, single-stapled anastomosis avoids cross-stapling, overcoming the potential drawbacks of transabdominal rectal transection and double-stapled anastomosis. This study aims to compare the anastomotic leak rate in double-stapled and single-stapled anastomoses after minimally invasive total mesorectal excision for magnetic resonance imaging-defined low rectal cancer.</p><p><strong>Methods: </strong>Adult patients (>18 years old) undergoing minimally invasive total mesorectal excision for magnetic resonance imaging-defined low rectal cancer with a stapled low anastomosis (below 5 centimeters from the anal verge) between January 2010 and January 2022 at a single institution were allocated to 2 groups according to the anastomosis: double-stapled (abdominal stapled transection and double-stapled anastomosis) or single-stapled (transanal rectal transection and double-pursestring single-stapled anastomosis). The exclusion criteria were nonrestorative procedures or any type of manual anastomosis. The primary endpoint was the rate of 90-day clinical and radiologic anastomotic leak.</p><p><strong>Results: </strong>In total, 185 single-stapled and 458 double-stapled were included. Clinical and tumor characteristics were comparable between the groups. The 90-day anastomotic leak rate was significantly lower in the single-stapled group (6.48% vs 15.28%; P = .002), with similar rates of grade and timing. Thirty- and 90-day complication rates were higher in the double-stapled group (P = .0001; P = .02), with comparable Clavien-Dindo grades. At multivariable analysis, double-stapled anastomosis (P = .01), active smoking (P = .03), and the presence of comorbidities (P = .01) resulted as independent risk factors for an anastomotic leak.</p><p><strong>Conclusion: </strong>Transanal transection and double-pursestring, single-stapled anastomosis were associated with a lower anastomotic leak rate after minimally invasive total mesorectal excision for magnetic resonance imaging-defined low rectal cancer.</p>
MRI dataset for susceptibility-based radiomic feature extraction in healthy controls and patients with multiple sclerosis
<p>This dataset include multiparametric MRI brain images and susceptiblity-based radiomic features within a mixed sample of 100 patients with multiple sclerosis and 50 healthy controls. We focused on the normal appearing white matter and its tracts. <br>Imaging and radiomic data are organized according to the Brain Imaging Directory Structure (BIDS) and for each subjects the following data are provided: anatomical T1w and T2w images; DWI after the correction for EPI distortions and susceptibility effects, eddy currents, and signal dropout, b-values and b-vectors and registration matrix to T1w; QSM QSM reconstruction, already registered in T1w space, raw magnitude and phase maps for the 5 echo times and registration matrix to T1w; volumes of interest (normal appearing white matter and tracts, divided for the two hemispheres); 107 radiomic features for each volume of interest. All images were anonymized and de-identified. <br>Additionally, in the database there is the ‘code’ folder containing the available scripts used for the processing (image registration, radiomic feature extraction, and robustness evaluation) and the participants file (containing for each subject the ID number, age, sex, date of scan, clinical condition (‘HC’ and ‘MS’) and the of the DWI sequence used, i.e. single- or multi-shell) and the dataset description.</p> <p>Data were provided by the Functional and Molecular Neuroimaging Unit at IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy. <a href="https://doi.org/10.5281/zenodo.11278906">Here</a> you can find the Data Use Agreement (DUA) to sign before using the dataset, together with the instructions to have the access.</p> <p>More details about image acquisition and processing may be found in Fiscone et al. (2024) Multiparametric MRI database for susceptibility-based radiomic feature extraction and analysis, <em>Scientific Data</em>. Please cite this paper if you use this dataset for publications or presentations. For any further information, please email cristiana.fiscone@gmail.com, davidneil.manners@unibo.it or giovanni.sighinolfi3@unibo.it. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.