| --- |
| 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. |
|
|
