---
library_name: ilex
tags:
- jax
- equinox
- ilex
- neuroimaging
- simultaneous
license: gpl-3.0
license_link: https://www.gnu.org/licenses/gpl-3.0.en.html
---

# ASL Denoising Autoencoder (Hales et al. -- ASL difference image denoising) -- ASL DAE published checkpoint

## Description

patrickhales/asl-denoising (Hales et al., *JMRI* 2020) is a
5-Conv2D U-Net-shaped denoising autoencoder for arterial spin
labelling (ASL) MRI difference images (dM). The model takes
single-repetition raw dM images (high-noise, single label-
control subtraction) and emits a denoised dM image
approximating the multi-repetition mean (low-noise, averaged
over typically 10 repetitions). The model also suppresses
transient artefacts from head motion, arterial-transit
variation, and spurious perfusion signals that survive a
single-rep subtraction.

v0 ships **one variant** (the upstream's released
``DaeTrainedModel.h5``; trained on 28,820 dM images over 67
early-stopped epochs of a planned 100). The architecture is
parameter-fixed at 149,441 trainable scalars.


## Intended use

ASL difference-image denoising + transient-artefact suppression. Input: (1, 128, 128) z-normalised dM slice (the consumer applies the preprocessing constants from the bundle's _ilex.preprocessing block). Output: same shape, also z-normalised; the consumer un-normalises by output * std + mean. The model is shape-invariant for any (H, W) divisible by 4; the upstream's pipeline always resizes inputs to (128, 128) first.

## Usage

```python
from ilex.models.asl_denoising import AslDenoisingDAE
model = AslDenoisingDAE.from_pretrained('ilex-hub/asl_denoising.published.1')
```

## Authors

Hales P. W. (Great Ormond Street Hospital / UCL Institute of Child Health)

## Citation

Hales P. W., Pfeuffer J., Clark C. A. (2020). Combined denoising and suppression of transient artefacts in arterial spin labelling MRI using deep learning. Journal of Magnetic Resonance Imaging. doi:10.1002/jmri.27255.

### References

- Hales P. W., Pfeuffer J., Clark C. A. (2020). Combined denoising and suppression of transient artefacts in arterial spin labelling MRI using deep learning. *Journal of Magnetic Resonance Imaging*. doi 10.1002/jmri.27255.
- Upstream code + weights -- github.com/patrickhales/asl-denoising (GPL-3.0).

## License

HF Hub license tag: `gpl-3.0`

**Effective terms:** GPL-3.0-only. The upstream code + released Keras .h5 weights are licensed under GPL-3.0 at github.com/patrickhales/asl-denoising. The ilex JAX / Equinox port code is licensed under GPL-3.0 to preserve the upstream's terms; this port is NOT available under the Apache-2.0 track that covers ilex's permissive- licensed ports. Redistribution requires the GPL-3.0 source-code-availability obligation.

Upstream license reference: https://www.gnu.org/licenses/gpl-3.0.en.html

### Copyright

patrickhales/asl-denoising is copyright (c) Patrick Hales,
GPL-3.0-licensed on the code + the released Keras .h5
weights. The ilex JAX / Equinox port code is licensed under
GPL-3.0 to preserve the upstream's license; this port is NOT
available under the Apache-2.0 track that covers ilex's
permissive-licensed ports.


## Upstream source

Original weights / reference implementation: https://github.com/patrickhales/asl-denoising

## Provenance

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