This dataset contains the results of an online survey run in summer of 2017 on the research data management (RDM) practices and perceptions of researchers using magnetic resonance imaging (MRI) to study human neuroscience (N=144). The dataset includes responses to multiple choice questions ordered roughly according the phases of a typical research project including data collection, analysis, and sharing. It focuses on a range of RDM topics, including the type of data collected, software and tools used to analyze and manage data, and the degree to which data management practices are standardized within a research group. It also includes participant ratings on the maturity of their data management practices and those of the field at large on a 1-5 scale from ad hoc to refined and responses about perceptions of new scholarly communications practices including data sharing, data reuse, and Open Access publishing.The survey instrument used can be found at the link in the reference below.

A database which contains longitudinal structural MRIs, spectroscopy, DTI and correlated clinical/behavioral data from approximately 500 healthy, normally developing children, ages newborn to young adult.

Multiple Sclerosis Dataset, Brain Tumor Dataset MRI Object Detection & Segmentation Dataset

The dataset is created on the basis of Brain MRI Dataset

The dataset consists of .dcm files containing MRI scans of the brain of the person with a multiple sclerosis. The images are labeled by the doctors and accompanied by report in PDF-format.

The dataset includes 13 studies, made from the different angles which provide a comprehensive understanding of a multiple sclerosis as a condition.

MRI study angles in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9fe7ab0fb5e7d66b0028561d78258baf%2FFrame%2080.png?generation=1707937444108216&alt=media" alt="">

💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on our website to buy the dataset

Types of diseases and conditions in the full dataset:

• Cancer
• Multiple sclerosis
• Metastatic lesion
• Arnold-Chiari malformation
• Focal gliosis of the brain
• AND MANY OTHER CONDITIONS

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fae5d9eb364be2e6a718b1ed7ebaa8ba1%2Fezgif.com-animated-gif-maker.gif?generation=1707936352780047&alt=media" alt="">

The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated multiple sclerosis detection and segmentation, analyzing response to different treatments, and studying the relationship between imaging features and clinical outcomes.

OTHER MEDICAL BRAIN MRI DATASETS:

• Brain Tumor MRI Dataset
• Brain Anomaly Detection Dataset
• Normal Brain MRI Dataset
• Fazekas Detection - MRI Dataset

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset includes:

• ST000001: includes subfolders with 13 studies. Each study includes MRI-scans in .dcm and .jpg formats,
• DICOMDIR: includes information about the patient's condition and links to access files,
• Brain_MRI_2.pdf: includes medical report, provided by the radiologist,
• .csv file: includes id of the studies and the number of files

Medical reports include the following data:

• Patient's demographic information,
• Description of the case,
• Preliminary diagnosis,
• Recommendations on the further actions

All patients consented to the publication of data

Medical data might be collected in accordance with your requirements.

🚀 You can learn more about our high-quality unique datasets here

keywords: tumors, cloud, testing, glioma, related, pytorch, directories, science, improve, directory, malignant, classify, accuracy, level, classified, cancerous, magnetic, neural, resonance, mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning

Here, we present an extension to our previously published structural ultrahigh resolution T1-weighted magnetic resonance imaging (MRI) dataset with an isotropic resolution of 250 µm (https://www.nature.com/articles/sdata201732), consisting of multiple additional ultrahigh resolution contrasts. Included are up to 150 µm Time-of-Flight angiography, an updated 250 µm structural T1-weighted reconstruc-tion, 330 µm quantitative susceptibility mapping, up to 450 µm structural T2-weighted imag-ing, 700 µm T1-weighted back-to-back scans, 800 µm diffusion tensor imaging, one hour continuous resting-state functional MRI with an isotropic spatial resolution of 1.8 mm as well as more than 120 other structural T1-weighted volumes together with multiple corresponding proton density weighted acquisitions collected over ten years. All data are from the same participant and were acquired on the same 7 T scanner. The repository contains the unprocessed data as well as (pre-)processing results. The data were acquired in multiple studies with individual goals. This is a unique and comprehensive collection comprising a “human phantom” dataset. Therefore, we compiled, processed, and structured the data, making them publicly available for further investigation.

Database of human brain images derived from a realistic phantom and generated using a sophisticated MRI simulator. Custom simulations may be generated to match a user's selected parameters. The goal is to aid validation of computer-aided quantitative analysis of medical image data. The SBD contains a set of realistic MRI data volumes produced by an MRI simulator. These data can be used by the neuroimaging community to evaluate the performance of various image analysis methods in a setting where the truth is known. The SBD contains simulated brain MRI data based on two anatomical models: normal and multiple sclerosis (MS). For both of these, full 3-dimensional data volumes have been simulated using three sequences (T1-, T2-, and proton-density- (PD-) weighted) and a variety of slice thicknesses, noise levels, and levels of intensity non-uniformity. These data are available for viewing in three orthogonal views (transversal, sagittal, and coronal), and for downloading.

This dataset was curated in collaboration between the Computer Science and Engineering Department, University of Dhaka and the National Institute of Neuroscience, Bangladesh. It comprise 5,285 T1-weighted contrast- enhanced brain MRI images belonging to 38 categories. This work is accompanied by a paper found here http://arxiv.org/abs/2106.07333

This data set contains anonymised clinical MRI study, or a set of scans, of 515 patients with symptomatic back pains. Each patient data can have one or more MRI studies associated with it. Each study contains slices, i.e., individual images taken from either sagittal or axial view, of the lowest three vertebrae and the lowest three IVDs. The axial view slices are mainly taken from the last three IVDs – including the one between the last vertebrae and the sacrum. The orientation of the slices of the last IVD are made to follow the spine curve whereas those of the other IVDs are usually made in blocks – i.e., parallel to each other. There are between four to five slices per IVD and they begin from the top of the IVD towards its bottom. Many of the top and bottom slices cut through the vertebrae leaving between one to three slices that cut the IVD cleanly and show purely the image of that IVD. In most cases, the total number of slices in axial view ranges from 12 to 15. However, in some cases, there may be up to 20 slices because the study contains slices of more than last three vertebrae. The scans in sagittal view also vary but all contain at least the last seven vertebrae and the sacrum. While the number of vertebrae varies, each scan always includes the first two sacral links.

There are a total 48,345 MRI slices in our dataset. The majority of the slices have an image resolution of 320x320 pixels, however, there are slices from three studies with 320x310 pixel resolution. The pixels in all slices have 12-bit per pixel precision which is higher than the standard 8-bit greyscale images. Specifically for all axial-view slices, the slice thickness are uniformly 4 mm with centre-to-centre distance between adjacent slices to be 4.4 mm. The horizontal and vertical pixel spacing is 0.6875 mm uniformly across all axial-view slices.

The majority of the MRI studies were taken with the patient in Head-First-Supine position with the rests were taken with the patient in in Feet-First-Supine position. Each study can last between 15 to 45 minutes and a patient may have one or more study associated with them taken at a different time or a few days apart.

You can download and read the research papers detailing our methodology on boundary delineation for lumbar spinal stenosis detection using the URLs provided in the Related Links at the end of this page. You can also check out other dataset and source code related to this program from that section.

We kindly request you to cite our papers when using our data or program in your research.

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Group average map of FLAIR images in standard MNI space across 1,832 MRiShare subjects.

Collection description

This collection contains group average maps presented in the associated publication "The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students".

Subject species

homo sapiens

Modality

Structural MRI

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

A

The respective data is comprised of 5 different datasets of medical images collected by the contributors, which can be used for classifying Lung Cancer, Bone Fracture, Brain tumor, Skin Lesions, and Renal Malignancy, respectively. The data also includes multiple disease and malignancy images for the respective dataset. The classification for the diseases can be done by using ResNet50 CNN architecture and other DCNN models. This data is also been used in a research article by the contributor.

Brain Cancer MRI Object Detection & Segmentation Dataset

The dataset consists of .dcm files containing MRI scans of the brain of the person with a cancer. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 10 studies, made from the different angles which provide a comprehensive understanding of a brain tumor structure.

  MRI study angles in the dataset





  💴 For Commercial Usage: Full version of the dataset includes… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/brain-mri-dataset.

Database of MRI quantitative measurements gathered from literature and experimental studies, for tissues and synthetic materials.Additionally, a code base is provided to aid in finding MRI tissue relaxation times for a target field strength, and to provide functionality to solve for tissue mimic composition given target tissue relaxation times.

The Imaging Database for Epilepsy And Surgery (IDEAS)

Peter N. Taylor, Yujiang Wang, Callum Simpson, Vytene Janiukstyte, Jonathan Horsley, Karoline Leiberg, Beth Little, Harry Clifford, Sophie Adler, Sjoerd B. Vos, Gavin P Winston, Andrew W McEvoy, Anna Miserocchi, Jane de Tisi, John S Duncan

Magnetic resonance imaging (MRI) is a crucial tool to identify brain abnormalities in a wide range of neurological disorders. In focal epilepsy MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data. Herein, we release an open-source dataset of preprocessed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections, and detailed demographic information. The MRI scan data includes the preoperative 3D T1 and where available 3D FLAIR, as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age of onset of epilepsy, location of surgery, histopathology of resected specimen, occurrence and frequency of focal seizures with and without impairment of awareness, focal to bilateral tonic-clonic seizures, number of anti-seizure medications (ASMs) at time of surgery, and a total of 1764 patient years of post-surgical follow up. Crucially, we also include resection masks delineated from post-surgical imaging. To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of around 50%. Our imaging data replicates findings of group level atrophy in patients compared to controls. Resection locations in the cohort were predominantly in the temporal and frontal lobes. We envisage our dataset, shared openly with the community, will catalyse the development and application of computational methods in clinical neurology.

https://arxiv.org/abs/2406.06731

This release on OpenNeuro includes only raw T1w and FLAR scans. Fully processed data, including resection masks and other demographic information can be found at the following locations: https://www.cnnp-lab.com/ideas-data

• Bids https://figshare.com/s/07fca72410094bc49506 Raw T1w and FLAIR scans organised in BIDS format. Nifti and json descriptors included

• Masks https://figshare.com/s/31ab43d1829b12ac13e8 Resection masks for IDEAS cohort in native, and freesurfer orig.mgz space

• Freesurfer_brain https://figshare.com/s/39b61a1df5fa8443e3c4 skullstripped brain from freesurfer in nifti format

• Freesurfer_orig https://figshare.com/s/f13391a4161b807ce6b0 freesurfer orig.mgz converted to nifti format

• Freesurfer_zip https://figshare.com/s/b13b8bb41390d3f7a088 freesurfer surface and volumetric reconstructions

• Tables_stats_freesurfer https://figshare.com/s/010142dd51e37ba4e4e2 Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation.

• Tables_metadata https://figshare.com/s/bab70268afeb1071202b clinical and demographic metadata

• Table_resected https://figshare.com/s/097ba0e254e36f0eee52 table indicating the percentage of each brain region in the Desikan-Kiliany atlas subsequently resected by surgery.

• Tables_zscores https://figshare.com/s/8c086fc295a75f85e628 Freesurfer thickness, volume, and surface areas for the Desikan-Kiliany parcellation, z-scored against normative controls post-combat.

• Tables_group_effect https://figshare.com/s/323db205354788c4d1f0 Group effect size differences to controls

This is a continuously growing and improving database of high-quality neuroanatomically labeled MRI brain scans, created not by an algorithm, but by neuroanatomical experts. All results are checked and corrected. Regions of interest include the usual sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter. We also sub-divide the cortex into "parcellation units" that are defined by gyral and sulcal landmarks. There are 157 ROIs now and more to come.

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1,722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early aging. Methods The dataset is based on magnetic resonance imaging (MRI) data collected as part of MRiShare database. The anatomical and diffusion-weighted imaging data from 1,832 healthy subjects were processed as described in the associated publication. The dataset contains global imaging-derived phenotypes (IDPs) described in the paper.

This brain tumor dataset contains 3064 T1-weighted contrast-inhanced images with three kinds of brain tumor. Detailed information of the dataset can be found in the readme file.The README file is updated:Add image acquisition protocolAdd MATLAB code to convert .mat file to jpg images

The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal neuroimaging repositories, comprising 1.8 million brain scans from 38,945 subjects linked to neurophysiological recordings. This comprehensive dataset addresses critical limitations in neuroimaging research by providing unprecedented scale and diversity across pathologies and healthy controls. BIND integrates de-identified data from three major academic medical centers -- Massachusetts General Hospital, Brigham and Women's Hospital, and Stanford University Medical Center -- including 1,724,300 MRI scans (1.5T, 3T, and 7T), 54,154 CT scans, 5,720 PET scans, and 655 SPECT scans, converted to standardized NIfTI format following BIDS organization. The database spans the full age spectrum and encompasses diverse neurological conditions alongside healthy subjects. We deployed Bio-Medical Large Language Models to extract structured clinical metadata from 84,960 associated radiology reports, categorizing findings into standardized pathology classifications. All imaging data are linked to previously published EEG and polysomnography recordings from the Harvard Electroencephalography Database and Human Sleep Project, enabling unprecedented multimodal analyses. BIND is freely accessible for academic research through the Brain Data Science Platform (https://bdsp.io/). This resource facilitates large-scale neuroimaging studies, machine learning applications, and multimodal brain research to accelerate discoveries in clinical neuroscience.

This dataset was curated in collaboration with the National Institute of Neuroscience, Bangladesh.

The NIH Study of Normal Brain Development is a longitudinal study using anatomical MRI, diffusion tensor imaging (DTI), and MR spectroscopy (MRS) to map pediatric brain development.

This deidentified imaging dataset is comprised of raw k-space data in several sub-dataset groups. Raw and DICOM data have been deidentified via conversion to the vendor-neutral ISMRMRD format and the RSNA Clinical Trial Processor, respectively. Manual inspection of each DICOM image was also performed to check for the presence of any unexpected protected health information (PHI), with spot checking of both metadata and image content.

Knee MRI: Data from more than 1,500 fully sampled knee MRIs obtained on 3 and 1.5 Tesla magnets and DICOM images from 10,000 clinical knee MRIs also obtained at 3 or 1.5 Tesla. The raw dataset includes coronal proton density-weighted images with and without fat suppression. The DICOM dataset contains coronal proton density-weighted with and without fat suppression, axial proton density-weighted with fat suppression, sagittal proton density, and sagittal T2-weighted with fat suppression.

Brain MRI: Data from 6,970 fully sampled brain MRIs obtained on 3 and 1.5 Tesla magnets. The raw dataset includes axial T1 weighted, T2 weighted and FLAIR images. Some of the T1 weighted acquisitions included admissions of contrast agent.

Additional information on file structure, data loader, and transforms are available on GitHub.

Prostate MRI: Data obtained on 3 Tesla magnets from 312 male patients referred for clinical prostate MRI exams. The raw dataset includes axial T2-weighted and axial diffusion-weighted images for each of the 312 exams.