---
title: FOMO45K
canonical_url: "https://www.modelscope.cn/datasets/FOMO-MRI/FOMO45K"
md_url: "https://www.modelscope.cn/datasets/FOMO-MRI/FOMO45K.md"
repository: FOMO-MRI/FOMO45K
last_updated: 2026-06-27
license: cc-by-nc-sa-4.0
storage_size: "1.3 TB"
downloads: 47
stars: 0
---

# FOMO45K

> FOMO45K - FOMO-MRI 在 ModelScope 开源的数据集。FOMO-45K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data

FOMO-MRI/FOMO45K 是 ModelScope 魔搭社区上的数据集，存储大小 1.3 TB，采用 cc-by-nc-sa-4.0 许可。

- **Repository**: FOMO-MRI/FOMO45K
- **License**: cc-by-nc-sa-4.0
- **Storage size**: 1.3 TB
- **Downloads**: 47
- **Stars**: 0
- **Last updated**: 2026-06-27

Source: https://www.modelscope.cn/datasets/FOMO-MRI/FOMO45K

---

# FOMO-45K: Brain MRI Dataset for Large-Scale Self-Supervised Learning with Clinical Data


![fomo45k](https://cdn-uploads.huggingface.co/production/uploads/67fe896341c831de7c499f73/FLaSwXgqX7lx3ODcMkaJ-.png)

_Dataset paper preprint:_ **A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning**.<br/>
[https://arxiv.org/pdf/2506.14432](https://arxiv.org/pdf/2506.14432).

This repo contains FOMO45K containing ~45K brain MRI scans.
This dataset is a subset of [FOMO60K](https://huggingface.co/datasets/FOMO-MRI/FOMO50K) that formed the basis for the **FOMO25: Foundation Model Challenge for Brain MRI** hosted at **MICCAI 2025**.

See [fomo25.github.io](https://fomo25.github.io) and [this paper](https://arxiv.org/abs/2604.11679) for more information about the challenge.

We also provide [FOMO-260K](https://huggingface.co/datasets/FOMO-MRI/FOMO260K), a larger version with over 260K scans without co-registration and skull-stripping.

## Description

FOMO45K is a large-scale dataset of brain MRI scans, including both clinical and research-grade scans. The dataset includes a wide range of sequences, including _T1, MPRAGE, T2, T2*, FLAIR, SWI, T1c, PD, DWI, ADC, and more_.

The dataset consists of
- 9,490 subjects
- 11,967 sessions
- 46,149 scans.

_For further details about the dataset, including the preprocessing, please consult the [dataset paper preprint](https://arxiv.org/pdf/2506.14432)_.

## Format

All data is provided as NIfTI-files. The dataset is provided as a collection of datasets, each within its own folder `PTXYZ_DatasetName` (e.g., `PT005_IXI`).
All data has been standardized and preprocessed (including skull stripped, RAS reoriented, co-registered) with the following format:

```
-- PT005_IXI
   |-- sub_102
       |-- ses_1
           |-- t1.nii.gz
-- PT006_SOOP
   |-- sub_44
       |-- ses_1
           |-- t1.nii.gz
```
Sessions with multiple scans of the same sequence are named `sequence_x.nii.gz`. Sessions where the sequence information was not available are named `scan_x.nii.gz`.

The dataset has been collected from the following public sources: BraTS-GEN, MSD-BrainTumor, IXI, NKI, SOOP, NIMH, DLBS, IDEAS, ARC, MBSR, UCLA, QTAB, AOMIC ID1000.

## Metadata Files
The dataset includes the following metadata files, available both in the main folder and in each individual dataset folder:

- `participants.tsv`: Contains demographic and clinical information including age, gender, handedness, and subject group (e.g., control, specific diagnosis)
- `mapping.tsv`: Links the files in FOMO-45K to the original data source scans
- `mri_info.tsv`: Includes MRI acquisition information

Additionally, the main folder contains:
- `FOMO45K_260K_mapping.tsv`: Maps the files that are present in both the 45K and 260K versions of the dataset

## Citation

Users must cite the following paper when using the FOMO45K dataset:

```bibtex
@article{Cerri2026large,
  title={A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning},
  author={Cerri, Stefano and Munk, Asbj{\o}rn and Llambias, Sebastian N{\o}rgaard and Ambsdorf, Jakob and Machnio, Julia and Nersesjan, Vardan and Hedeager Krag, Christian and Liu, Peirong and Rocamora Garc{\'\i}a, Pablo and Mehdipour Ghazi, Mostafa and Boesen, Mikael and Benros, Michael Eriksen and Iglesias, Juan Eugenio and Nielsen, Mads},
  journal={arXiv preprint arXiv:2506.14432},
  year={2026},
  url={https://arxiv.org/abs/2506.14432}
}
```

In addition, users must comply with all attribution requirements of the constituent datasets included in FOMO45K, as specified in the Usage Notes of the paper.