README.md

---
license: odc-by
language:
  - en
task_categories:
  - image-classification
tags:
  - medical
  - brain-data
  - mri
pretty_name: 3D Brain Structure MRI Scans
---

## 🧠 Dataset Summary
3794 anonymized 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. 3529 scans come from cognitively normal individuals and 265 scans from individuals with an Alzheimer's disease clinical diagnosis. Scan image 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 include 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
<table>
  <tr>
    <td align="center">
      <img src="sample_images/18_F_CN_2966.png" alt="18_F_CN_2966" width="150">
      <br>Age 18 F, NKI-RS
      <br>Cognitively normal
    </td>
    <td align="center">
      <img src="sample_images/71_M_AD_3585.png" alt="71_M_AD_3585" width="150">
      <br>Age 71 M, OASIS-1
      <br>Alzheimer's disease
    </td>
    <td align="center">
      <img src="sample_images/46_F_CN_436.png" alt="46_F_CN_436" width="150">
      <br>Age 46 F, IXI
      <br>Cognitively normal
    </td>
    <td align="center">
      <img src="sample_images/86_M_CN_3765.png" alt="86_M_CN_3765" width="150">
      <br>Age 86 M, OASIS-2
      <br>Cognitively normal
    </td>
  </tr>
</table>


# 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/
 ├─ 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
pip install datasets

# optional installs: NiBabel and PyTorch
pip install nibabel
pip install torch torchvision
```

```
# load datasets
from datasets import load_dataset

ds_train = load_dataset("radiata-ai/brain-structure", split="train", trust_remote_code=True)
ds_val = load_dataset("radiata-ai/brain-structure", split="validation", trust_remote_code=True)
ds_test = load_dataset("radiata-ai/brain-structure", split="test", trust_remote_code=True)
```

```
# example PyTorch processing of images
import nibabel as nib
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset

def preprocess_nifti(example):
    """
    Loads a .nii.gz file, crops, normalizes, and resamples to 96^3.
    Returns a numpy array (or tensor) in example["img"].
    """
    nii_path = example["nii_filepath"]
    # Load volume data
    vol = nib.load(nii_path).get_fdata()

    # Crop sub-volume
    vol = vol[7:105, 8:132, :108]  # shape: (98, 124, 108)

    # Shift intensities to be non-negative
    vol = vol + abs(vol.min())
    # Normalize to [0,1]
    vol = vol / vol.max()

    # Convert to torch.Tensor: (1,1,D,H,W)
    t_tensor = torch.from_numpy(vol).float().unsqueeze(0).unsqueeze(0)

    # Scale factor based on (124 -> 96) for the y-dimension
    scale_factor = 96 / 124
    downsampled = F.interpolate(
        t_tensor,
        scale_factor=(scale_factor, scale_factor, scale_factor),
        mode="trilinear",
        align_corners=False
    )

    # Now pad each dimension to exactly 96 (symmetric padding)
    _, _, d, h, w = downsampled.shape
    pad_d = 96 - d
    pad_h = 96 - h
    pad_w = 96 - w
    padding = (
        pad_w // 2, pad_w - pad_w // 2,
        pad_h // 2, pad_h - pad_h // 2,
        pad_d // 2, pad_d - pad_d // 2
    )
    final_img = F.pad(downsampled, padding)  # shape => (1, 1, 96, 96, 96)
    final_img = final_img.squeeze(0)

    # Store as numpy or keep as torch.Tensor
    example["img"] = final_img.numpy()
    return example
```

```
# Apply the preprocessing to each split
ds_train = ds_train.map(preprocess_nifti)
ds_val   = ds_val.map(preprocess_nifti)
ds_test  = ds_test.map(preprocess_nifti)

# Set the dataset format to return PyTorch tensors for the 'img' column
ds_train.set_format(type='torch', columns=['img'])
ds_val.set_format(type='torch', columns=['img'])
ds_test.set_format(type='torch', columns=['img'])

# Set up data loaders for model training
train_loader = DataLoader(ds_train, batch_size=16, shuffle=True)
val_loader   = DataLoader(ds_val, batch_size=16, shuffle=False)
test_loader  = DataLoader(ds_test, batch_size=16, shuffle=False)
```


# 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: Processed Structural MRI Brain Scans Across the Lifespan},
  year      = {2025},
  url          = {https://huggingface.co/datasets/radiata-ai/brain-structure},
  note      = {Version 1.0},
  publisher    = {Hugging Face}
}
```