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FARD

Image-to-Image
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mri
medical-imaging
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FARD
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---
license: cc-by-nc-4.0
tags:
  - mri
  - medical-imaging
  - super-resolution
  - image-restoration
  - multi-contrast
library_name: pytorch
pipeline_tag: image-to-image
---

# FARD — Frequency Attention Residual Denoising

Weights for **FARD**, the multi-contrast fusion network from *Multi-Contrast MRI
Acceleration via Post-Reconstruction Fusion* (Medical Image Analysis, in press).

FARD refines vendor-reconstructed magnitude images from complementary orthogonal
phase-encoding acquisitions. It is **reference-free**: it needs no fully-sampled
high-resolution contrast to guide it, and no access to raw k-space. Each contrast
is acquired with a reduced phase-encoding matrix along a different axis, so the
three inputs lose resolution along different directions and jointly retain the
spatial-frequency content needed to restore all of them.

## Files

Three checkpoints, one per output contrast. Each takes all three accelerated
contrasts as input and predicts its own target.

| File | Output contrast | Params |
|---|---|---|
| `fard_t1n.safetensors` | T1-weighted | 0.504 M |
| `fard_t2w.safetensors` | T2-weighted | 0.504 M |
| `fard_flair.safetensors` | T2-FLAIR | 0.504 M |

These are weights only — no optimizer or scheduler state. `checksums.json`
carries the SHA-256 of each file and the MD5 of the original `.pth` it was
converted from, so provenance against the source repository is checkable.

## Architecture

A 2D slice-wise network: 10 convolutional layers at C=64 with 3×3 kernels,
block-wise gated attention at layers 2, 5 and 8, and frequency-domain processing
at layers 3, 6 and 9. Residual connections every three layers. The three
accelerated inputs are concatenated early and projected to C channels.

Where the parameters sit:

| Component | Params |
|---|---|
| backbone (10 conv layers) | 0.369 M |
| attention modules | 0.084 M |
| frequency modules | 0.041 M |
| fusion + aux conv + final layer | 0.010 M |
| **total** | **0.504 M** |

## Usage

The architecture lives in the code repository; these files are the weights.

```python
from safetensors.torch import load_file

model.load_state_dict(load_file("fard_t1n.safetensors"))  # strict=True
```

Channel order matters. Each checkpoint expects its own target contrast's
accelerated input first:

| Output | Input channel order |
|---|---|
| T1n | `t1n, t2w, flair` |
| T2w | `t2w, flair, t1n` |
| FLAIR | `flair, t1n, t2w` |

Feeding a different order will produce plausible-looking but wrong output, since
nothing in the network makes the ordering explicit.

## Inputs

Bicubic-upsampled, vendor-reconstructed magnitude images, registered across
contrasts and resampled to a common 1 mm³ grid. The code repository's
`data_prep/` reproduces that three-stage registration — intra-contrast, then
inter-contrast to an anchor, then anchor to MNI.

## Intended use and limitations

Research use only. This is not a medical device and has not been evaluated for
clinical decision-making.

Trained and evaluated on brain MRI at approximately 4× protocol acceleration:
a prospectively acquired 1.5T cohort, and retrospective simulation on BraTS-GLI
2024. Behaviour outside that regime — other anatomy, other field strengths, other
acceleration factors, other vendors' reconstruction — is uncharacterised.

The network refines an existing reconstruction rather than reconstructing from
k-space, so it cannot recover information the accelerated acquisition did not
capture. On T1 in axial evaluation the degraded axis is through-plane, which
limits what a slice-wise 2D model can restore.

## Citation

```bibtex
@article{nazarov2026multicontrast,
  title   = {Multi-Contrast MRI Acceleration via Post-Reconstruction Fusion},
  author  = {Nazarov, Alexander and Kiryati, Nahum and Roizen, Dani and
             Kerpel, Ariel and Hoffmann, Chen and Greenberg, Gahl and
             Mayer, Arnaldo},
  journal = {Medical Image Analysis},
  year    = {2026},
  note    = {In press}
}
```

## License

CC BY-NC 4.0 — non-commercial use, with attribution. This matches the license on
the FARD architecture in the code repository; the surrounding pipeline there is
MIT.