--- license: odc-by language: - en task_categories: - image-classification tags: - medical - brain-data - mri pretty_name: 3D Brain Structure MRI Scans --- ## 🧠 Dataset Summary 3794 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: [DLBS](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html), [IXI](https://brain-development.org/ixi-dataset/), [NKI-RS](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing_neuro.html), [OASIS-1](https://sites.wustl.edu/oasisbrains/home/oasis-1/), and [OASIS-2](https://sites.wustl.edu/oasisbrains/home/oasis-2/). Subjects have a mean age of 45 ± 24. 3773 scans come from cognitively normal individuals and 261 scans from individuals with an Alzheimer's disease clinical diagnosis. Scans dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see methods). Scans have been processed and all protected health information (PHI) is excluded. Only the skull-stripped scan, integer age, biological sex, clinical diagnosis, and scan metadata are included. [Radiata](https://radiata.ai/) compiles and processes publicly available neuroimaging datasets to create this open, unified, and harmonized dataset. For more information see https://radiata.ai/public-studies. Example uses including developing foundation-like models or tailored models for brain age prediction and disease classification. # License The use of the dataset as a whole is licensed under the ODC-By v1.0 license. Individual scans are licensed under study-specific data use agreements: IXI - [CC BY-SA 3.0](https://brain-development.org/ixi-dataset/) DLBS - [CC BY-NC 4.0](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html) NKI-RS - [Custom DUA](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing.html) OASIS-1 - [Custom DUA](https://sites.wustl.edu/oasisbrains/) OASIS-2 - [Custom DUA](https://sites.wustl.edu/oasisbrains/) The metadata provide the license for each object. # Sample images
18_F_CN_2966
Age 18 F, NKI-RS
Cognitively normal 		71_M_AD_3585
Age 71 M, OASIS-1
Alzheimer's disease 		46_F_CN_436
Age 46 F, IXI
Cognitively normal 		86_M_CN_3765
Age 86 M, OASIS-2
Cognitively normal
# Subject characteristics table | Split | n (scans) | n (subjects) | age_mean | age_std | age_range | sex_counts | diagnosis_counts | study_counts | |-------|-----------|--------------|-----------|-----------|-------------|--------------------------------|--------------------------|----------------------------------------------------------------------------| | train | 3066 | 2085 | 45.1 | 24.5 | (6, 98) | {'female': 1827, 'male': 1239} | {'CN': 2847, 'AD': 219} | {'NKI-RS': 1854, 'OASIS-1': 340, 'IXI': 326, 'OASIS-2': 296, 'DLBS': 250} | | validation | 364 | 261 | 46.4 | 24.5 | (6, 90) | {'female': 225, 'male': 139} | {'CN': 339, 'AD': 25} | {'NKI-RS': 213, 'IXI': 43, 'OASIS-1': 38, 'OASIS-2': 38, 'DLBS': 32} | | test | 364 | 261 | 45.7 | 24.6 | (6, 93) | {'female': 210, 'male': 154} | {'CN': 343, 'AD': 21} | {'NKI-RS': 216, 'IXI': 40, 'OASIS-2': 39, 'OASIS-1': 36, 'DLBS': 33} | # Folder organization ```bash brain-structure/ β”œβ”€ brain-structure.py β”œβ”€ metadata.csv β”œβ”€ IXI/ β”‚ β”œβ”€ sub-002/ β”‚ β”‚ └─ ses-01/ β”‚ β”‚ └─ anat/ β”‚ β”‚ β”œβ”€ msub-002_ses-01_T1w_brain_affine_mni.nii.gz β”‚ β”‚ └─ msub-002_ses-01_scandata.json β”‚ └─ ... β”œβ”€ DLBS/ β”‚ └─ ... └─ ... ``` # Example usage ``` # install Hugging Face Datasets library pip install datasets ``` ``` # load datasets from datasets import load_dataset ds_train = load_dataset("radiata-ai/brain-structure", name="all", split="train", trust_remote_code=True) ds_val = load_dataset("radiata-ai/brain-structure", name="all", split="validation", trust_remote_code=True) ds_test = load_dataset("radiata-ai/brain-structure", name="all", split="test", trust_remote_code=True) ``` ``` # example PyTorch processing of images import torch import torch.nn.functional as F from torch.utils.data import Dataset import nibabel as nib class NiiDataset(Dataset): """ A PyTorch Dataset that wraps a Hugging Face Dataset containing MRI .nii.gz file paths, and loads/normalizes/resamples each volume. """ def __init__(self, hf_dataset): """ hf_dataset: a Hugging Face Dataset object, e.g. ds_train (each example should have a 'nii_filepath') """ self.hf_dataset = hf_dataset def __len__(self): return len(self.hf_dataset) def __getitem__(self, idx): # Load the dataset example example = self.hf_dataset[idx] nii_path = example["nii_filepath"] # e.g., "IXI/sub-017/ses-01/anat/sub-017_ses-01_T1w.nii.gz" # Load the .nii.gz file with nibabel cur_t1_file = nib.load(nii_path) t1_data = cur_t1_file.get_fdata() # Preprocess: example sub-volume # (7:105, 8:132, :108) => shape: (98, 124, 108) t1_data = t1_data[7:105, 8:132, :108] # Normalize intensities t1_data = t1_data / t1_data.max() # Convert to PyTorch tensor, add two channel dims for volumetric interpolation # shape => (1, 1, 98, 124, 108) t1_tensor = torch.from_numpy(t1_data).float().unsqueeze(0).unsqueeze(0) # Downsample/resample to e.g. (96, 96, 96) img_downsample = F.interpolate( t1_tensor, size=(96, 96, 96), mode="trilinear", align_corners=False ) # shape => (1, 96, 96, 96) # Squeeze out the channel dim if needed: shape => (96, 96, 96) img_downsample = img_downsample.squeeze(0) sample = {"img": img_downsample} return sample ``` # Study descriptions - IXI: A dataset of nearly 600 MR images from normal, healthy subjects, including T1, T2, PD-weighted, MRA, and diffusion-weighted images collected at three different hospitals in London. Citation: IXI data was obtained from https://brain-development.org/ixi-dataset/ - DLBS: A dataset from the Dallas Lifespan Brain Study (DLBS) comprising structural MRI, DTI, functional MRI, resting-state fMRI, and amyloid PET scans from 350 healthy adults aged 20-89, including extensive cognitive testing and demographic information. Citation: DLBS data was obtained from the International Neuroimaging Data-sharing Initiative (INDI) database. - NKI-RS: A large-scale ongoing neuroimaging dataset (N > 1000) across the lifespan from a community sample, including structural and functional MRI scans such as MPRAGE, DTI, resting-state fMRI, and task-based fMRI. Citation: NKI-RS data was obtained from Rockland Sample Neuroimaging Data Release. - OASIS-1: Cross-sectional T1-weighted MRI data from 416 right-handed subjects aged 18 to 96, including 100 over 60 with very mild to moderate Alzheimer’s disease, each with 3 or 4 scans. Citation: OASIS-1: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498 - OASIS-2: A longitudinal MRI dataset of 150 right-handed individuals aged 60-96, with 373 imaging sessions including T1-weighted MRIs, featuring nondemented and demented older adults, including patients with Alzheimer’s disease. Citation: OASIS-2: Longitudinal: https://doi.org/10.1162/jocn.2009.21407 # Methods ## Image processing T1-weighted structural MRI scans were processed with [CAT12](https://neuro-jena.github.io/cat12-help/) ([Gaser et al, 2024](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520)). The image processing steps were: - correct for bias, noise, and intensity - mask to brain-only (gray matter + white matter + CSF) - register to ICBM 2009c Nonlinear Asymmetric space (MNI152NLin2009cAsym 1.5mm^3) using linear affine registration with 12 degrees of freedom in [FSL FLIRT](https://fsl.fmrib.ox.ac.uk/fsl/docs/#/registration/flirt/index) ('flirt -in t1.nii.gz -ref mni_icbm152_t1_tal_nlin_asym_09c_brain_1_5_mm.nii.gz -dof 12 -noresampblur'). The goal was to get denoised, unsmoothed scans that were maximally aligned to standard space while preserving individual anatomy. Metadata includes the total intracranial volume (TIV), image quality rating (IQR; larger value = worse quality), MRI scanner manufacturer/model, and field strength. ## Train/validation/test partitioning Scans were partitioned into train/validation/test datasets with a 80%/10%/10% split. Splits were balanced for age, sex, clinical diagnosis, and study. Subjects with multiple scans only appear in one split. # Citation ``` @dataset{Radiata-Brain-Structure, author = {Jesse Brown and Clayton Young}, title = {Brain-Structure: A Collection of Processed Structural MRI Scans}, year = {2025}, url = {https://huggingface.co/datasets/radiata-ai/brain-structure}, note = {Version 1.0}, publisher = {Hugging Face} } ```