---
library_name: ilex
tags:
- jax
- equinox
- ilex
- neuroimaging
- brain
license: mit
license_link: https://opensource.org/license/mit/
---

# BME-X (brain MRI enhancement foundation model) -- Month18-T2

## Description

BME-X (Sun et al., Nature Biomedical Engineering 2025), ported to JAX / Equinox from the DBC-Lab PyTorch release (github.com/DBC-Lab/Brain_MRI_Enhancement). A 3-level dual-head dense U-Net ("Anatomy-Guided Densely-Connected U-Net", ADU-Net) that runs a tissue classifier and a reconstruction head in parallel, bridged by a connection block that feeds the seg-derived tissue probability map back into the reconstruction pathway. The network is designed for motion correction, resolution enhancement, denoising, harmonization (cross-scanner), and 3T -> 7T-like image generation in one forward pass. Inputs are single-channel T1- or T2-weighted brain MRI patches at 32^3 voxels (the upstream's sliding-window inference tiles 64^3 patches with 0.85 overlap; the network is fully convolutional over any multiple-of-8 spatial grid). Outputs are (a) a 4-channel seg head (per-voxel relu output over BG / WM / GM / CSF; apply softmax for probabilities) and (b) a 1-channel reconstructed/enhanced volume. The published release has 15 age-and-modality-specific weight files spanning fetal-T2 through Month-24-T1/T2; per the paper, Month24 weights are also used for adult inference.

## Intended use

T2 enhancement for ~18-month brain MRI.

## Usage

```python
from ilex.models.bme_x import BMEX
model = BMEX.from_pretrained('ilex-hub/bme_x.month18-t2.1')
```

## Authors

Sun Y., Wang L., et al. (DBC Lab, University of North Carolina at Chapel Hill)

## Citation

Sun Y., Wang L., et al. (2025). A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks. Nature Biomedical Engineering. doi:10.1038/s41551-024-01283-7.

### References

- Sun Y., Wang L., Li G., Lin W., Liu M., Wang Y., He X., Wang Z., Wei Z., Han Z., Tian Y., Niu J., Cui Z., Zhu D., Yang Y., Zhao L., Wei Z., Quinn B. T., Schiratti J.-B., Zhu Y., Wei H., Wang W., Tao Y., Zhang Y., Wang Z., Cui Z., Bashyam V., Wen J., Tassinari G., Davatzikos C., Mowery D. L., Zhou Z., Yan J., Sun L., Shen D., Wang L. (2025). A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks. Nature Biomedical Engineering. doi:10.1038/s41551-024-01283-7.
- Upstream code: github.com/DBC-Lab/Brain_MRI_Enhancement (BME_X/models/DUNet3D_seg_recon_softmax.py).

## License

HF Hub license tag: `mit`

Upstream license reference: https://opensource.org/license/mit/

### Copyright

Network architecture and pretrained weights -- copyright (c) the DBC-Lab BME-X authors, released under the MIT License. JAX / Equinox port code -- copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.

## Upstream source

Original weights / reference implementation: https://github.com/DBC-Lab/Brain_MRI_Enhancement

## Provenance

This artefact was produced by [ilex](https://github.com/hypercoil/ilex)'s
save/load pipeline. The architecture is implemented in
`ilex.models.bme_x.BMEX` and the weights have been converted
from their upstream format. See the upstream source above
for the canonical reference.