README.md

Description

3794 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: IXI, DLBS, NKI-RS, OASIS-1, and 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 are processed and no protected health information (PHI) is included - only the skull-stripped scan, integer age, biological sex, and clinical diagnosis. Radiata 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
DLBS - CC BY-NC 4.0
NKI-RS - Custom DUA
OASIS-1 - Custom DUA
OASIS-2 - Custom DUA

The metadata provide the license for each object.

Sample images

Age 18 F, NKI-RS Cognitively normal

Age 71 M, OASIS-1 Alzheimer's disease

Age 46 F, IXI Cognitively normal

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

brain-structure/
 ├─ brain-structure.py          
 ├─ metadata.csv                
 ├─ IXI/
 │   ├─ sub-002/
 │   │   └─ ses-01/
 │   │       └─ anat/
 │   │           ├─ msub-002_ses-01_T1w_brain_affine_mni.nii.gz
 │   │           └─ msub-002_ses-01_scandata.json
 │   └─ ...
 ├─ DLBS/
 │   └─ ...
 └─ ...

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 (Gaser et al, 2024). 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 ('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 }
}