Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
73
datasets available to search
ShareScore release 0.9.0
Dataset results
73 results for “MRI dataset”
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>
LiverHccSeg: A Publicly Available Multiphasic MRI Dataset with Liver and HCC Tumor Segmentations and Inter-Rater Agreement Analysis
<p>Please <strong>cite our data paper </strong>published in "Data in Brief": https://www.sciencedirect.com/science/article/pii/S2352340923007473</p><p> </p><p><strong>Background</strong><br>Liver cancer ranks as the third leading cause of cancer-related mortality worldwide [1] and alarmingly, both the incidence and mortality rates of liver cancer are increasing [2; 3]. Among the various types of primary liver cancer, hepatocellular carcinoma (HCC) stands out as the most prevalent, accounting for approximately 70-85% of liver cancer cases [4]. Leveraging the advantages of magnetic resonance (MR) imaging, HCC can be reliably detected and diagnosed without the requirement of an invasive biopsy [5]. MR imaging offers high tissue contrast, which can be further enhanced through contrast-enhanced multiphasic magnetic resonance imaging (mpMRI) techniques. This enables accurate identification and non-invasive diagnosis of HCC [6].</p><p> </p><p><strong>Objective</strong><br>Precise segmentation of the liver plays a crucial role in volumetry assessment and serves as a vital pre-processing step for subsequent tumor detection algorithms [7]. However, accurate liver segmentation can be particularly challenging in patients with cancer-related tissue alterations and deformations in shape [8]. Accurate HCC tumor segmentation is essential for the extraction of quantitative imaging biomarkers such as radiomics and can be used for studies on treatment response assessment and prognosis evaluation and provides critical information about the tumor biology. In order to enhance the reproducibility of liver and tumor segmentation, automated methods utilizing image analysis techniques and machine learning have been developed. These methods have demonstrated promising results [7; 8]; however, most algorithms were tested only on small internal test sets and therefore do not guarantee generalizable and consistent performance on external data.</p><p>Publicly available datasets allow for fair and objective comparisons between different algorithms, techniques, or approaches. Researchers can evaluate the strengths and weaknesses of their methods in relation to existing solutions and establish benchmarks for performance evaluation. In addition to providing a benchmark with this dataset, we also assess the inter-rater variability between two different sets of tumor segmentations. This analysis serves as a measure of reproducibility for human segmentations, highlighting the consistency or variability that may exist among different human raters. Understanding the reproducibility of human segmentations is essential in assessing the reliability of manual annotations and establishing a baseline for algorithm performance comparison. By introducing LiverHccSeg, we aim to fill the gap of lacking publicly available mpMRI HCC datasets and offer researchers and developers a valuable resource for algorithmic evaluation on external data and imaging biomarker analyzes.</p><p> </p><p><strong>Materials and Methods</strong></p><p><i><strong>Inclusion of Patients</strong></i><br>All available scans from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) (<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436">https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436</a>) were downloaded [9]. One multiphasic MRI scan (pre and triphasic post contrast) per patient was included. Patients who did not exhibit a tumor or residual tumor were excluded from the tumor segmentation dataset; however, they were included in the liver segmentation dataset.</p><p> </p><p><i><strong>MR Imaging Data</strong></i><br>Subsequently, all imaging data was converted to the Neuroimaging Informatics Technology Initiative (NIfTI) format with the dcm2nii (v2.1.53) package [10] and available header information was extracted using the pydicom (v.2.1.2) package [11]. Multiparametric MR sequences were labeled with a consistent syntax ('pre', 'art', 'pv', 'del', for the pre-contrast, arterial, portal-venous and delayed contrast phases, respectively). All images were already de-identified by the TCIA website. Images were acquired between the years 1993 and 2007 on Philips and Siemens scanners with field strengths of 1.5 and 3 Tesla. Full details of the imaging parameters can be found in Table 5. Briefly, the median repetition time (TR) and median echo time (TE) were 365.8 ms and 26.4 ms, respectively. The median slice thickness was 9.5 mm, the median bandwidth 536.9 Hz.</p><p> </p><p><i><strong>Scientific Reading</strong></i><br>After conversion, all images were read in a scientific reading by two board-certified abdominal radiologists (S.A. and S.H with 9 and 10 years of experience, respectively). Any disagreement between the two raters was discussed in a consensus meeting. All HCC lesions were classified according to LI-RADS criteria [6].</p><p> </p><p><i><strong>Image Registration</strong></i><br>The co-registration of pre-contrast, portal-venous, and delayed-phase images with arterial phase images was performed using the software BioImage Suite (v3.5) [12]. A non-rigid intensity-based registration approach was applied, employing a parameterized free-form deformation (FFD) with 3D B-splines [13]. The optimal FFD transformation was estimated by maximizing the normalized mutual information similarity metric [14] through gradient descent optimization. To enhance the optimization process, a multi-resolution image pyramid with three levels was utilized. The final B-spline control point spacing was set to 80 mm. The estimated transformation was then employed to warp the moving images (pre-contrast, portal-venous, and delayed-phase) into the reference image space, specifically the arterial phase image.</p><p> </p><p><i><strong>Liver and Tumor Segmentation and Statistical Analysis</strong></i><br>All livers and tumors were manually segmented under the supervision of two board-certified abdominal radiologists using the software 3D Slicer (v4.10.2) [15]. To compare the segmentation agreement between the two sets of liver and tumor segmentations, we calculated segmentation metrics using the Python package seg-metrics (v1.0.0) [16]. All segmentation metrics and statistics were calculated in Python (v3.7).</p><p> </p><p><strong>Data description</strong><br>The data that appears in this article include:</p><ol><li>dicoms.zip: This zip file contains all the raw MR images from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) [1] in the Digital Imaging and Communications in Medicine (DICOM) format used for the curation of this dataset. The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique unidentified patient ID, DATE is the date of the image acquisition, and SEQUENCE is the name of the MR sequence.<br> </li><li>LiverHccSeg_MetaData.xlsx: This spreadsheet contains all the metadata from the DICOM headers along with the data from the scientific image readings.<br> </li><li>nifti_and_segms.zip: This zip file contains all MR images along with the liver and tumor segmentations in the Neuroimaging Informatics Technology Initiative (NIfTI) format.<br>The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique anonymized patient identifier, DATE is the date of the image acquisition, and SEQUENCE is the name of the MRI sequence or segmentation image.<br><br>The NIfTI files are named as follows:<br><strong>pre.nii.gz</strong> : Pre-contrast T1-weighted MRI<br><strong>art.nii.gz</strong>: Arterial-phase T1-weighted MRI<br><strong>pv.nii.gz</strong>: Portal-venous-phase T1-weighted MRI<br><strong>del.nii.gz</strong>: Delayed-phase T1-weighted MRI<br><strong>art_pre.nii.gz</strong>: Pre-contrast T1-weighted MRI registered to the corresponding arterial-phase T1-weighted image<br><strong>art_pv.nii.gz</strong>: Portal-venous-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRI<br><strong>art_del.nii.gz</strong>: Delayed-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRI<br><br>The corresponding manual segmentations are named after the rater and the type of segmentation and follow the format 'RATER_ROI.nii.gz' where RATER denotes the human rater and ROI denotes the region of interest that was segmented, for example, '<strong>rater1_liver.nii.gz</strong>', '<strong>rater2_liver.nii.gz</strong>', '<strong>rater1_tumor1.nii.gz</strong>', and '<strong>rater2_tumor1.nii.gz</strong>'. For tumor segmentations, an integer indicates the tumor identification number for different tumor ROIs, for example, 'rater1_tumor1.nii.gz' and 'rater2_tumor1.nii.gz'. The segmentations can be used for the arterial phase NIfTI file as well as the corresponding co-registered pre-contrast (art_pre.nii.gz), portal-venous (art_pv.nii.gz), and delayed-phase (art_del.nii.gz) images.<br> </li><li>segm_metrics.xlsx: This spreadsheet summarizes the segmentation agreement between the two sets of liver and tumor segmentations by the two board-certified abdominal radiologists.</li></ol><p> </p><p><strong>References</strong></p><p>1 Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-249</p><p>2 Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7-34</p><p>3 White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB (2017) Incidence of Hepatocellular Carcinoma in All 50 United States, From 2000 Through 2012. Gastroenterology 152:812-820.e815</p><p>4 Perz JF, Armstrong GL, Farrington LA, Hutin YJ, Bell BP (2006) The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide. J Hepatol 45:529-538</p><p>5 Hamer OW, Schlottmann K, Sirlin CB, Feuerbach S (2007) Technology insight: advances in liver imaging. Nat Clin Pract Gastroenterol Hepatol 4:215-228</p><p>6 Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 289:816-830</p><p>7 Bousabarah K, Letzen B, Tefera J et al (2020) Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol. 10.1007/s00261-020-02604-5</p><p>8 Gross M, Spektor M, Jaffe A et al (2021) Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 16:e0260630</p><p>9 Erickson BJ, Kirk S, Lee Y et al (2016) Radiology Data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma [TCGA-LIHC] collection. The Cancer Imaging Archive. 10.7937/K9/TCIA.2016.IMMQW8UQ</p><p>10 dcm2nii DICOM to NIfTI converter. <a href="https://github.com/rordenlab/dcm2niix">https://github.com/rordenlab/dcm2niix</a> Accessed: 2021-12-07.</p><p>11 Mason D, scaramallion;, rhaxton; et al (2020) pydicom/pydicom: pydicom 2.1.2, v2.1.2. Zenodo</p><p>12 X. Papademetris MJ, N. Rajeevan, H. Okuda, R.T. Constable, L.H Staib BioImage Suite: An integrated medical image analysis suite, Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine. <a href="http://www.bioimagesuite.org">http://www.bioimagesuite.org</a>.</p><p>13 Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans Med Imaging 18:712–721</p><p>14 Studholme C, Hill DL, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32:71-86</p><p>15 Fedorov A., Beichel R., Kalpathy-Cramer J. et al (2012) 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323-1341</p><p>16 Ordgod (2020) Ordgod/segmentation_metrics: seg-metrics, v1.0.0. Zenodo</p><p> </p><p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</p><p><strong>ChangeLog</strong></p><p><strong>Version 1.1:</strong> Fixed incorrect liver segmentation mask.</p><p> </p><p> </p><p> </p><p> </p>
UTHealth - Endometriosis MRI Dataset (UT-EndoMRI)
<h2>Introduction</h2> <p>Magnetic Resonance Imaging (MRI) is widely recommended as a primary non-invasive diagnostic tool for endometriosis. Endometriomas affect 17–44% of women diagnosed with the condition. Accurate MRI-based ovary segmentation in endometriosis patients is essential for detecting endometriomas, guiding surgery, and predicting post-operative complications. However, ovary segmentation becomes challenging when the ovary is deformed or absent, often due to surgical resection, emphasizing the need for highly experienced clinicians. An automatic segmentation pipeline for pelvic MRI in endometriosis patients could greatly reduce the manual workload for clinicians and help standardize ovary segmentation.</p> <p>The UTHealth Endometriosis MRI Dataset (UT-EndoMRI) includes multi-sequence MRI scans and structural labels collected from two clinical institutions, Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women. The first dataset comprises MRI scans and labels from 51 patients collected before 2022, featuring T2-weighted and T1-weighted fat-suppressed MRI sequences. The uterus, ovaries, endometriomas, cysts, and cul-de-sac structures were manually segmented by three raters. The second dataset, collected in 2022, consists of MRI scans and labels from 82 endometriosis patients. These sequences include T1-weighted, T1-weighted fat suppression, T2-weighted, and T2-weighted fat suppression MRI. In this dataset, the uterus, ovaries, and endometriomas were manually contoured by a single rater. Using these datasets, we investigated interrater agreement and developed an automatic ovary segmentation pipeline, RAovSeg, for endometriosis.</p> <p>The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184). The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research. </p> <p> </p> <p> </p> <h2>Endometriosis MRI</h2> <p>This dataset includes MRI scans and labels from two clinical institutions. The data from the first institution can be found in the ```D1_MHS/ ```directory, while the data from the second institution are located in the ```D2_TCPW/``` directory. Each subfolder contains MRI scans and corresponding labels from different raters.</p> <p>The naming conventions for the files are as follows:</p> <p>MRI scans:<br>D[dataset ID]- [patient ID] _ [MRI sequence].nii.gz</p> <p>Anatomical structure labels:<br>D[dataset ID]- [patient ID] _ [structure name] _ r[rater ID].nii.gz</p> <p>For the labels in the ```D2_TCPW/ ```directory, since they were generated by a single rater, there is no rater ID included in the file names.</p> <p>The abbreviations used for naming:<br>T1: T1-weighted MRI<br>T1FS: T1-weighted fat suppression MRI<br>T2: T2-weighted MRI<br>T2FS: T2-weighted fat suppression MRI<br>ov: ovary<br>ut: uterus<br>em: endometrioma<br>cy: cyst<br>cds: cul de sac</p> <p>For example, the file located at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_T1FS.nii.gz```represents the T1-weighted fat suppression MRI for subject 000 in dataset 1. The file at ```UT-EndoMRI/D1_MHS/D1-000/D1-000_ ut_r1.nii.gz``` is the uterus segmentation manually contoured by rater 1 for subject 000 in dataset 1. The file at```UT-EndoMRI/ D2_TCPW/D2-006/D2-006_ cy.nii.gz``` is the cyst segmentation manually contoured for subject 006 in dataset 2.</p> <p>MRI sequences may be missing due to a lack of acquisition.</p> <p>The MR Scanner information for Dataset 1 is available in 'SiteScannerInfo.csv'.</p> <p> </p> <h2>Train/Validation/Test Replication</h2> <p>The data split for RAovSeg training, validation, and testing is provided as follows:<br>- Training/validation subjects IDs: D2-000 – D2-007<br>- Testing subjects IDs: D2-008 – D2-037<br>All data in dataset 1, as well as other data in dataset 2, are not used in RAovSeg development.</p> <p> </p> <h2>Data Acquisition</h2> <p>This dataset was acquired at the Texas Medical Center, within the Memorial Hermann Hospital System and the Texas Children’s Hospital Pavilion for Women. The study and the data sharing were approved by the Committee for the Protection of Human Subjects at UTHealth (protocol no. HSC-SBMI-22-0184).</p> <p> </p> <h2>User Agreement</h2> <p>The UT-EndoMRI dataset is available for free use exclusively in non-commercial scientific research. Any publications resulting from its use must cite the following paper.<br><br><a href="https://www.nature.com/articles/s41597-025-05623-3" target="_blank" rel="noopener">Liang, X., Alpuing Radilla, L.A., Khalaj, K. <em>et al.</em> A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis. <em>Sci Data</em> <strong>12</strong>, 1292 (2025). https://doi.org/10.1038/s41597-025-05623-3</a></p> <h2>Funding</h2> <p>This work has been supported by the Robert and Janice McNair Foundation.</p> <p> </p> <h2>Research Team</h2> <p>Here are the people behind this data acquisition effort:<br>Xiaomin Liang, Linda A Alpuing Radilla, Kamand Khalaj, Haaniya Dawoodally, Chinmay Mokashi, Xiaoming Guan, Kirk E Roberts, Sunil A Sheth, Varaha S Tammisetti, Luca Giancardo</p> <h2>Acknowledgements</h2> <p>We would also like to acknowledge for their support: Memorial Hermann Hospital System and Texas Children’s Hospital Pavilion for Women.</p>
mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
<p>The ability to estimate 3D human body pose and movement, also known as human pose estimation~(HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few datasets exploit multiple modalities and focus on home-based health monitoring.</p> <p>To bridge this gap, we present <em>mRI</em>, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 5 million frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality.</p> <p>We hope that the release of <em>mRI</em> can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly, facilitate the applications of home-based health monitoring.</p>
[Dataset for] Whole-brain meso-vein imaging in living humans using fast 7 T MRI
<p>This dataset is associated with:</p> <ul> <li>Gulban, Stirnberg, Tse, Pizzuti, Koiso, Archila-Melendez, Huber, Bollmann, Goebel, Kay, Ivanov, 2025. Whole-brain meso-vein imaging in living humans using fast 7 T MRI (Preprint).</li> </ul> <p>This dataset is also used in:</p> <ul> <li>Pizzuti, Bazin, Ivanov, Dresbach, Peter, Goebel, Gulban, 2024. Multimodal laminar characterization of visual areas along the cortical hierarchy (Preprint).</li> </ul> <p>More data are going to be be added as we progress with our manuscripts though their publications or upon request (please contact Omer Faruk Gulban).</p>
TotalSegmentator MRI dataset: 616 MRI images with segmentations for 50 anatomical regions
<p>In 616 MR images we segmented 50 anatomical structures covering a majority of relevant classes for most use cases. The MR images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. Moreover, it contains some images from <a href="https://portal.imaging.datacommons.cancer.gov/" target="_blank" rel="noopener">IDC</a> for further data diversity (see column "source" in meta.csv).</p> <p>Link to a copy of this dataset on Dropbox for much quicker download: <a href="https://www.dropbox.com/scl/fi/gskhmz2n9mmlt7rcn77wo/TotalsegmentatorMRI_dataset_v200.zip?rlkey=kf1eocb6xjmqwzafuohhv19by&st=ka3m6wae&dl=0" target="_blank" rel="noopener">Dropbox Link</a></p> <p>You can find a segmentation model trained on this dataset <a href="https://github.com/wasserth/TotalSegmentator">here</a>.<br><br>More details about the dataset can be found in the corresponding <a href="https://arxiv.org/abs/2405.19492">paper</a>. Please cite this paper if you use the dataset. The CT images described in the paper can be found <a href="../doi/10.5281/zenodo.6802613" target="_blank" rel="noopener">here</a>.</p> <p>This dataset contains all 50 structures from the TotalSegmentator "total" task. It does not contain the structures of other TotalSegmentator MRI subtasks.</p> <p>This dataset was created by the department of <a href="https://www.unispital-basel.ch/en/radiologie-nuklearmedizin/forschung-radiologie-nuklearmedizin">Research and Analysis at University Hospital Basel</a>.</p> <p><strong>UPDATE</strong>: on 2025-01-21 we uploaded version 2.0.0 which increases the number of images from 298 to 616. It also contains slightly different structures.</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>
Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology
<p><span>H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we provided deep convolutional neural networks, called MRH-Nets, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. </span><span> </span>The dataset contains multi-contrast MRI and histology used for the training and testing and the acquisition parameters. The datasets have been carefully registered to mouse brain images from the Allen Mouse Brain Atlas (https://mouse.brain-map.org). The source codes for MRH-Nets can be found at <a href="https://github.com/liangzifei/MRH-Net">https://github.com/liangzifei/MRH-Net</a>.</p>
Dataset: Towards a representative reference for MRI-based human axon radius assessment using light microscopy
<p>Dataset for: Towards a representative reference for MRI-based human axon radius assessment using light microscopy</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>
Cardiovascular MRI (CMR) 3D Cine, 4D Flow and Exercise Stress 4D flow datasets
<p>MRI Datasets for "<strong>Motion-robust free-running volumetric cardiovascular MRI" </strong>(<a href="https://doi.org/10.1002/mrm.30123">https://doi.org/10.1002/mrm.30123</a>)</p> <p><strong>Reconstruction Code Repository</strong>: <a href="https://github.com/OSU-MR/motion-robust-CMR">https://github.com/OSU-MR/motion-robust-CMR</a><br><br><strong>Cite as</strong>: Arshad SM, Potter LC, Chen C, et al. Motion-robust free-running volumetric cardiovascular MRI. Magn Reson Med. 2024; 92(3): 1248-1262. doi: 10.1002/mrm.30123<br> </p> <p>Contains: </p> <ol> <li>3D cine Cartesian bSSFP Acquisition (Undersampled data sorted into 20 cardiac bins)</li> <li>4D flow Cartesian Acquisition (Undersampled data sorted into 20 cardiac bins)</li> <li>Stress 4D flow Cartesian Acquisition (Undersampled data sorted into 20 cardiac bins)</li> </ol> <p><br><strong>Prepared by</strong>:</p> <p><strong>Syed Murtaza Arshad</strong> (<a href="arshad.32@osu.edu">arshad.32@osu.edu</a>)</p> <p>(<a href="https://github.com/syedmurtazaarshad">https://github.com/syedmurtazaarshad</a>)</p> <p> </p>
brain-tumor-mri-dataset
<p>Brain Tumor MRI Dataset from Kaggle: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset</p> <p>Author: Msoud Nickparvar</p>
Example dataset for the MRI_Convert_Nikon_Andor_To_Hyperstack script
<p>The data-set contains one image with two channels. Each channel is broken down into 6 separate files. There are for each channel a total of 330 frames and 31 z-slices for each frame.</p>
Dataset related to article "Gradient-induced vibrations and motion-induced Lenz effects on conductive nonmagnetic orthopedic implants in MRI"
<p>Set of data reported in the tables and figures of the article entitled "Gradient-induced vibrations and motion-induced Lenz effects on conductive nonmagnetic orthopedic implants in MRI", published in Magnetic Resonance in Medicine with DOI:10.1002/mrm.30263.</p> <p><span>The results presented here have been developed in the framework of the 21NRM05 STASIS Project. The Project has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. </span></p>
Rat gadoxetate MRI signal dataset for the IMI-WP2-TRISTAN Six Test Compounds study
<p>Dataset to accompany the manuscript "Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug–Drug Interactions in Rats" by Nicola Melillo, Daniel Scotcher, J. Gerry Kenna, Claudia Green, Catherine D. G. Hines, Iina Laitinen, Paul D. Hockings, Kayode Ogungbenro, Ebony R. Gunwhy, Steven Sourbron, John C. Waterton, Gunnar Schuetz, and Aleksandra Galetin (referred to as the 'Six Test Compounds' study).</p> <p><strong>data.zip</strong></p> <p>After extracting the contents within the data.zip folder, the MRI signal data acquired in this study are provided in csv format and can be found inside the <code>data/SixTestCompounds/01_signals</code> subfolder. Each csv file contains the liver and spleen region of interest (ROI) time curve data for a single rat at a specific site and day, after administration of the test compound of interest.</p> <p>The corresponding filename for each file is formatted as a string containing study descriptors (metadata) separated by underscores, i.e., filename = <code>compound_site_RatNumber_dayNumber_dataType</code>, e.g., <code>Asunaprevir_E_Rat2_2_Signals</code>.</p> <p><strong>results.zip</strong></p> <p>The results and figures relating to this manuscript were created using the outputs contained in <code>results/SixTestCompounds/2022-09-01</code>.</p> <p><code>01_model_ouputs</code> contains all outputs generated as a result of the tracer kinetic model fitting. Within this, plotted signal time curves for each acquistion per rat may be found in <code>figures/per_rat</code>, while average deltaR1 curves per drug may be found in <code>figures/per_drug</code>. All estimated parameter variables from the fitting are stored in <code>all_parameters.csv</code>. The folder <code>relaxation_rates_and_signals</code> contains the fitted MRI signal data in a similar format to the original csv data in <code>data/01_signals</code>. Additional columns have been included showing the MRI signals converted to R1.</p> <p><code>02_effect_sizes</code> contains results of the statistical analysis performed on the tracer kinetic modelling output. This is summarised in tabular format in <code>effect_sizes.csv</code>, while graphical distributions are provided within the <code>figures/</code> folder. <code>fit_errors.txt</code> contains an ID list for any computational fitting errors found during quality control of the tracer kinetic modelling.</p>
DATASET RELATED TO ARTICLE "Muscle quantitative MRI in adult SMA patients on nusinersen treatment_ a longitudinal study"
<p>Excel file with raw data of muscle fat fraction before and after nusinersen treatment</p>
Rat gadoxetate MRI signal dataset for the IMI-WP2-TRISTAN Reproducibility study
<p>Dataset to accompany the manuscript "Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: A reproducibility study" by Ebony R. Gunwhy, Catherine D. G. Hines, Claudia Green, Iina Laitinen, Sirisha Tadimalla, Paul D. Hockings, Gunnar Schütz, J. Gerry Kenna, Steven Sourbron, and John C. Waterton (referred to as the 'Reproducibility' study).</p> <p><strong>data.zip</strong></p> <p>After extracting the contents within the data.zip folder, the MRI signal data acquired in this study are provided in csv format and can be found inside the <code>data/Reproducibility/01_signals</code> subfolder. Each csv file contains the liver and spleen region of interest (ROI) time curve data for a single rat from a specific substudy and day, after administration of the test compound of interest.</p> <p>The corresponding filename for each file is formatted as a string containing study descriptors (metadata) separated by underscores, i.e., filename = <code>substudy_</code><code>compound_site_RatNumber_dayNumber_dataType</code>, e.g., <code>1_Rifampicin_D_Rat5_2_Signals</code>.</p> <p><strong>results.zip</strong></p> <p>The results and figures relating to this manuscript were created using the outputs contained in <code>results/Reproducibility/2023-04-17</code>.</p> <p><code>01_model_ouputs</code> contains all outputs generated as a result of the tracer kinetic model fitting. Within this, plotted signal time curves for each acquistion per rat may be found in <code>figures/per_rat</code>, while average deltaR1 curves per substudy may be found in <code>figures/per_substudy</code>. All estimated parameter variables from the fitting are stored in <code>all_parameters.csv</code>. The folder <code>relaxation_rates_and_signals</code> contains the fitted MRI signal data in a similar format to the original csv data in <code>data/01_signals</code>. Additional columns have been included showing the MRI signals converted to R1. <code>fit_errors.txt</code> contains an ID list for any computational fitting errors found during quality control of the tracer kinetic modelling.</p> <p><code>02_analyses</code> contains results of the statistical analysis performed on the tracer kinetic modelling output. These are summarised in tabular csv format within the <code>repeatability/ </code>and <code>reproducibility/</code> folders, while graphical distributions are provided within the <code>figures/</code> folder. Summary results for benchmark values and from the mixed ANOVA are also provided in <code>benchmarks.csv </code>and<code> mixed_anova.csv</code>, respectively. Please see the manuscript for further details on these results.</p>
Whole-Brain 3D MRF and MRI Multi-Scanner, Scan-Rescan Dataset at 3T
<p>This repository contains a multi-scanner, scan-rescan dataset comprising thirty (30) whole-brain 3D MRI acquisitions. Each scan acquisition (n = 30) includes whole-brain quantitative MRI maps (MRF T1, MRF T2, and ADC) and weighted MR images (T1w MPRAGE, T2w SPACE, and T2w FLAIR). Five healthy subjects (deidentified) were scanned over three scanners (all 3T field strength). Each subject was scanned twice per scanner, for a total of 30 scans (5 subjects × 3 scanners × 2 scans). All data was acquired at University Hospitals Cleveland Medical Center and Case Western Reserve University.</p> <p> </p> <p>This dataset was used in the analysis for the following research article published in Investigative Radiology: "<strong><a href="https://journals.lww.com/investigativeradiology/fulltext/9900/physics_informed_discretization_for_reproducible.159.aspx">Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI</a></strong>" and request this article be referenced in all research works that use this dataset.</p>
Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology
Open the record for dataset details and reuse information.
mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.