---
title: 'FOMO60K: Heterogeneous 3D Brain MRI Dataset'
url: https://www.emergentmind.com/topics/fomo60k
type: topic
---

# FOMO60K: Heterogeneous 3D Brain MRI Dataset

Searching arXiv for the most relevant FOMO60K papers and closely related usage papers.
FOMO60K is a large-scale, heterogeneous 3D brain magnetic resonance imaging dataset assembled to support self-supervised learning, foundation-model pretraining, and downstream neuroimaging method development at scale. It comprises **60,529 MRI scans** from **13,900 sessions** across **11,187 subjects**, aggregated from **16 publicly available sources**, and includes both clinical- and research-grade data, multiple MRI sequence types, and substantial anatomical and pathological variability, including scans with large brain anomalies [2506.14432]. Subsequent work positioned FOMO60K as the pretraining substrate for the FOMO25 challenge on clinical brain MRI foundation models and also used its NKI cohort as the sole imaging source for diffusion-based brain MRI super-resolution experiments [2604.11679][2603.14667].

## 1. Definition and intended scope

FOMO60K was introduced as a **large-scale, heterogeneous dataset of 3D brain MRI** designed specifically for **self-supervised learning (SSL)** and benchmarking in neuroimaging. Its stated motivation is to enable ImageNet-scale-style pretraining for volumetric medical imaging while preserving the heterogeneity of real clinical and multi-cohort data. The dataset is intended to lower barriers to entry through minimal preprocessing, standardized NIfTI release, and accompanying code for pretraining and finetuning [2506.14432].

In the FOMO25 challenge, FOMO60K served as the central unlabeled pretraining corpus for brain MRI foundation models. The challenge explicitly used it to study whether SSL on a large, heterogeneous corpus improves robustness under domain shift and in few-shot downstream regimes. In the method track, participants were required to pretrain exclusively on FOMO60K so that differences in downstream performance could be attributed to architectural and objective choices rather than differences in pretraining data [2604.11679].

A common misconception is to treat FOMO60K as a conventional curated benchmark with heavy harmonization and narrow modality scope. The published description indicates the opposite: the dataset was intentionally built to retain scanner-, site-, and protocol-specific variation, and it spans multiple sequence families rather than a single structural contrast [2506.14432].

## 2. Dataset composition and heterogeneity

FOMO60K aggregates data from **16 public datasets** spanning healthy cohorts, aging and neurodegeneration studies, stroke and aphasia cohorts, epilepsy surgery data, tumor datasets, and susceptibility-focused acquisitions. The included sequence types are **T1**, **T2**, **FLAIR**, **DWI**, **T1ce**, **PD**, **SWI**, **GRE**, **minIP**, and **T2*** [2506.14432].

| Source dataset | Scans | Sequences / status |
|---|---:|---|
| MGH Wild | 11,366 | Unknown; skull-stripped |
| SOOP | 6,508 | T1, FLAIR, DWI; defaced |
| BraTS-GEN | 9,004 | T1, T2, FLAIR, T1ce; skull-stripped |
| OASIS1 | 1,688 | T1; skull-stripped |
| OASIS2 | 1,326 | T1; skull-stripped |
| MSD Brain Tumor | 1,936 | T1, T2, FLAIR, T1ce; skull-stripped |
| IXI | 2,530 | T1, T2, PD, DWI; skull-stripped |
| NIMH Healthy Research Volunteer | 2,089 | T1, T2, T2*, DWI; skull-stripped |
| DLBS | 3,845 | T1, T2, DWI; skull-stripped |
| IDEAS | 1,035 | T1, FLAIR; defaced |
| ARC | 2,029 | T1, T2, DWI; defaced |
| MBSR | 1,023 | T1, DWI; defaced |
| UCLA LA5c | 789 | T1, DWI; defaced |
| QTAB | 6,760 | minIP, GRE, SWI; defaced |
| NKI | 4,502 | T1, T2, DWI; defaced |
| AOMIC ID1000 | 4,099 | T1, DWI; defaced |

The dataset’s heterogeneity is explicit along several axes. First, it combines **clinical and research-grade** images. Second, it spans multiple scanners, vendors, field strengths, and acquisition protocols. Third, it includes substantial anatomical and pathological diversity, including **gliomas, metastases, meningiomas, stroke, aphasia, epilepsy-related surgical cases, aging-related cohorts**, and scans with large anomalies. The challenge paper reiterates that FOMO60K is “highly heterogeneous in terms of subject demographics, scanner vendors and models, field strengths, acquisition protocols, and MRI sequence types” [2604.11679].

This composition has methodological consequences. It makes FOMO60K substantially closer to heterogeneous clinical archives than narrowly curated research datasets, but it also means that nuisance variation is preserved rather than normalized away. A plausible implication is that representation learning methods pretrained on FOMO60K are pressured to learn invariances that are directly relevant to cross-site and out-of-domain deployment.

## 3. Data format, organization, and preprocessing

All released scans are provided in **NIfTI-compressed** format (`.nii.gz`). Subjects are organized as `sub_X`, sessions as `ses_Y`, and files within sessions are named by sequence type where metadata are available, such as `t1.nii.gz` or `flair.nii.gz`; repeated acquisitions are enumerated, and scans with unavailable sequence metadata may appear as `scan_X.nii.gz`. Subject identifiers were shuffled to prevent straightforward remapping to original cohort IDs [2506.14432].

The preprocessing pipeline is intentionally minimal and harmonized rather than aggressively standardized. All scans were reoriented to **RAS orientation** using `mri_coreg` from **FreeSurfer 7.4.1** with default parameters. Within each session, scans were **affinely co-registered to the highest spatial resolution scan**, which aligns multi-sequence data without warping images into a common atlas space. For diffusion MRI, 4D data were converted into structurally usable 3D volumes by extracting **b=0** when available and by selecting and averaging **b=1000** directions most closely aligned with the canonical axes using cosine similarity to $\mathbf{e}_x$, $\mathbf{e}_y$, and $\mathbf{e}_z$ [2506.14432].

Face removal and de-identification combine pre-existing defacing or skull-stripping from source datasets with additional skull-stripping using **SynthSeg** when needed. Skull-stripping was applied to images not already skull-stripped or defaced, or when visual inspection detected residual cranial features that could compromise anonymization. The descriptor also states what was **not** done: there is no normalization to a common template such as MNI, no global intensity normalization or histogram matching, no dataset-level cropping or padding to fixed dimensions, and no advanced bias-field correction pipeline is described at the FOMO60K level [2506.14432].

These choices preserve raw scanner- and protocol-specific characteristics. That design is deliberate: FOMO60K is not a homogenized atlas-space resource, but rather a standardized release of heterogeneous volumetric data suitable for SSL pipelines that can tolerate variable voxel spacings, matrix sizes, and modality availability.

## 4. Role in self-supervised learning and the FOMO25 challenge

FOMO60K’s central research role is as a pretraining corpus for SSL-based brain MRI foundation models. In the FOMO25 challenge, the **method track** constrained participants to pretrain **only on FOMO60K**, whereas the **open track** allowed any pretraining data. Downstream evaluation was performed on three clinical tasks: **infarct classification**, **meningioma segmentation**, and **brain-age regression**, under few-shot and out-of-domain conditions [2604.11679].

The challenge paper organizes pretraining strategies into **local objectives** such as masked autoencoding, **global objectives** such as contrastive learning, and **hybrid objectives** combining reconstruction and contrastive terms. Representative method-track submissions included MAE-based models, SimMIM-style masked image modeling, MoCo v2-style multimodal contrastive learning, and hybrid VAE-plus-contrastive systems. The paper also reports diverse augmentation and sampling choices on FOMO60K, including random flips, rotations, scaling, elastic deformations, patch sampling between $64^3$ and $160^3$, Gaussian or Rician noise, blur, sharpening, gamma transforms, bias field, and Gibbs-ringing or resampling-artifact simulation [2604.11679].

The main empirical findings are dataset-level rather than model-specific. **Sixteen of nineteen** evaluated foundation models outperformed the **Supervised-OOD** baseline trained from scratch on the same few-shot data, and **two method-track models** pretrained only on FOMO60K outperformed even the **Supervised-ID** baseline trained on larger in-domain Danish data. The challenge also reports that **no single pretraining objective benefits all tasks**: local MAE-like objectives tended to favor segmentation, while hybrid reconstruction-contrastive objectives favored classification and regression. In addition, improvements from scaling model size and training duration did **not** yield reliable benefits [2604.11679].

These findings are important because they detach the value of FOMO60K from a single winning architecture. The evidence suggests that FOMO60K is large and heterogeneous enough to support substantial gains under clinical domain shift, but that objective design and adaptation strategy remain decisive.

## 5. Use of the NKI cohort for MRI super-resolution

Although FOMO60K was created primarily for SSL, later work used its **NKI cohort** as the **sole** MRI source for training and evaluating elucidated diffusion models for brain MRI super-resolution. That study describes the NKI cohort as providing **T1-weighted structural brain MRI volumes from over 1,300 subjects**, each acquired at approximately **1 mm isotropic resolution** and stored in **NIfTI** format. The authors trained on **59 subjects (100 scanning sessions)** and tested on **5 subjects (6 sessions, 993 sagittal slices)**, with a subject-level split to prevent leakage [2603.14667].

The super-resolution formulation used synthetic low-resolution data derived from FOMO60K high-resolution volumes. Volumes were intensity-normalized to **$[0,255]$ using the 1st--99th percentile range**, sliced along the **sagittal axis**, and each 2D slice was downsampled by a factor of **2** using **block averaging** to produce a **$128\times128$** low-resolution input paired with a **$256\times256$** high-resolution target. For 3D training, low- and high-resolution volume pairs were stored directly and random patches of size **$32^3$** in LR and **$32\times64\times64$** in HR were extracted during training. All tensors were then normalized to **$[-1,1]$** [2603.14667].

Within this setting, the paper compared a **full 3D convolutional U-Net** with volumetric patches and multi-head self-attention against a **2.5D slice-conditioned U-Net** that super-resolves each slice independently while conditioning on one adjacent slice. On the held-out test set, the **3D EDM** achieved **37.75 dB PSNR**, **0.997 SSIM**, and **0.020 LPIPS**, outperforming both an off-the-shelf pretrained **EDSR** baseline (**35.57 dB**, **0.024 LPIPS**) and the **2.5D EDM** (**35.82 dB PSNR**) under the same test data and degradation pipeline [2603.14667].

The study is also explicit about scope limitations. Its evaluation is confined to a **single cohort** within FOMO60K, uses **image-domain block-average downsampling**, and does **not** model k-space truncation, noise, or motion artifacts typical of clinical acquisition. The paper therefore treats the NKI subset as a clean, standardized high-resolution source rather than as a full proxy for heterogeneous clinical low-resolution MRI [2603.14667].

## 6. Limitations, ethics, access, and future directions

FOMO60K has several documented limitations. Because it aggregates only **public datasets**, its demographic and disease distributions are not guaranteed to match real clinical prevalence. The dataset paper notes likely underrepresentation of some geographic, racial, and ethnic groups, uneven age distributions across cohorts, and technical imbalance across modalities, with some sequences much more frequent than others. It also highlights metadata gaps, especially for **MGH Wild**, where many scans have **unknown** sequence labels [2506.14432].

Ethically, the dataset is constructed from public de-identified data. De-identification relies on a combination of existing defacing or skull-stripping, additional **SynthSeg**-based skull-stripping where needed, and manual inspection for residual cranial features. The MRI super-resolution study likewise states that it uses **de-identified human brain MRI data** from the publicly available FOMO60K/NKI dataset and that the work involves only secondary analysis of anonymized data [2506.14432][2603.14667].

Access and licensing are described across the companion publications. The dataset descriptor lists the Hugging Face release at `https://huggingface.co/datasets/FOMO25/FOMO-MRI`, while the FOMO25 challenge paper lists `https://huggingface.co/datasets/FOMO-MRI/FOMO60K`. The challenge paper states that the FOMO60K pretraining dataset is released under **CC BY-NC-SA 4.0**, with constituent datasets retaining their original licenses; users are therefore expected to respect both the aggregate release conditions and the license terms of the source cohorts [2506.14432][2604.11679].

Future work around FOMO60K is already sketched in the literature. The FOMO25 paper frames it as first-edition infrastructure for future challenge rounds and further work on 3D self-distillation, transformer-based approaches, and better balancing of local and global SSL objectives. The MRI super-resolution paper identifies **scaling to the full FOMO60K dataset ($\sim 60{,}000$ subjects)** as a concrete future direction [2604.11679][2603.14667].

Taken together, these publications establish FOMO60K as more than a static data release. It is a heterogeneous pretraining substrate, a benchmark infrastructure for foundation models under domain shift, and a reusable source of volumetric MRI for downstream tasks that require large-scale, minimally preprocessed brain imaging data.

Source: https://www.emergentmind.com/topics/fomo60k