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README.md

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 ---
-license: apache-2.0
+license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: image-to-image
+tags:
+  - medical-imaging
+  - mri
+  - brain
+  - brats
+  - missing-modality
+  - image-synthesis
+  - 3d
+  - wacv2027
 ---
+
+# PRIMED weights
+
+Weights for **PRIMED** (PRior-Informed Missing-modality synthesis by Endpoint Displacement), from the WACV 2027
+paper
+
+**When Iteration Cannot Help: Single-Pass Missing-Modality MRI Synthesis and What Actually Limits It**<br>
+Atharva Atul Rege, Adinath Madhavrao Dukre, Imran Razzak
+
+Code: [github.com/adinathdukre/PRIMED](https://github.com/adinathdukre/PRIMED)
+
+PRIMED synthesises a missing MRI contrast from the acquired ones in a single network evaluation, with no
+noise schedule and no solver. It works in a fixed 3D Haar wavelet basis and starts from the mean of the
+acquired contrasts (the informed prior). A 3D U-Net predicts the displacement from that prior to the target.
+A learned target-contrast embedding lets one set of weights synthesise every contrast.
+
+## Files
+
+| File | Training data | Synthesises | Released weights |
+|---|---|---|---|
+| `primed_brats.safetensors` | BraTS 2023 GLI, 1032 subjects | any of T1n, T1c, T2w, T2f from the other three | best checkpoint, step 15 000 of up to 20 000 |
+| `primed_brats_avail.safetensors` | as `primed_brats`, plus random input availability | any of the four from one, two or three acquired contrasts | final, 8 000 further steps |
+| `primed_brats_robust.safetensors` | as `primed_brats`, plus misregistration and thick-slice augmentation | any of the four from three possibly misregistered or thick-slice contrasts | final, 8 000 further steps |
+| `primed_ixi.safetensors` | IXI, 477 subjects | any of T1, T2, PD from the other two | best checkpoint, step 28 000 of up to 30 000 |
+
+Each file holds the network weights only, 97.7M parameters in float32 (about 391 MB). The two fine-tunes
+start from `primed_brats.safetensors` and are released with their final raw weights, as reported in the
+paper.
+
+## Usage
+
+```bash
+git clone https://github.com/adinathdukre/PRIMED.git
+cd PRIMED
+pip install -e .
+hf download adinathdukre/PRIMED --local-dir /path/to/weights
+
+python synthesize.py \
+  --weights /path/to/weights/primed_brats.safetensors \
+  --target t1c \
+  --inputs t1n=/path/to/subject-t1n.nii.gz t2w=/path/to/subject-t2w.nii.gz t2f=/path/to/subject-t2f.nii.gz \
+  --output /path/to/subject-t1c-synthesized.nii.gz
+```
+
+```python
+import torch
+from primed import load_model
+from primed.data import load_volume
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = load_model("/path/to/weights/primed_brats.safetensors", device)
+inputs = torch.stack(
+    [load_volume(f"/path/to/subject-{m}.nii.gz") for m in ("t1n", "t2w", "t2f")]
+).unsqueeze(0).to(device)
+prior = inputs.mean(dim=1)
+t1c = model.synthesize(prior, inputs, torch.tensor([1], device=device))
+```
+
+**Inputs.** Co-registered volumes on the 1 mm BraTS grid (240 x 240 x 155). The BraTS models expect
+skull-stripped volumes; `primed_ixi` expects volumes that are not skull-stripped, as IXI provides them. Each
+volume is clipped to its 0.1 and 99.9 percentiles, rescaled to [0, 1], padded to 160 slices and cropped in
+plane to 224 x 224; `load_volume` and `synthesize.py` do this automatically. The contrasts are, in class
+order:
+
+- BraTS: T1n, T1c, T2w, T2f;
+- IXI: T1, T2, PD.
+
+The acquired contrasts are passed in this order with the target left out. **Output:** the target contrast in
+the same normalised [0, 1] range.
+
+## Results
+
+On 216 held-out BraTS 2023 GLI subjects, `primed_brats` reaches:
+
+- whole-volume PSNR of 29.372 / 28.137 / 28.953 / 27.778 dB for T1 / T1c / T2 / FLAIR;
+- **28.560 dB on average**, with 0.9439 SSIM;
+- 0.152 s per volume on one NVIDIA RTX PRO 6000 Blackwell.
+
+On cohorts not seen in training, `primed_brats` reaches:
+
+- 26.555 dB on 60 BraTS-MEN training subjects;
+- 26.413 dB on the 141-subject BraTS-MEN validation release;
+- 26.398 dB on 99 BraTS-PED subjects.
+
+`primed_ixi` reaches 31.436 dB on 96 held-out IXI subjects.
+
+With fewer acquired contrasts, `primed_brats_avail` reaches 28.36 / 27.35 / 25.97 dB with three / two / one
+inputs. The zero-shot `primed_brats` reaches 28.56 / 25.40 / 22.37 dB.
+
+`primed_brats_robust` gives up 0.12 dB on registered inputs and reduces the loss under every perturbation
+tested, for example:
+
+| Inputs | `primed_brats` | `primed_brats_robust` |
+|---|:-:|:-:|
+| one input misregistered by 2 mm, 2° | 26.60 dB | 27.28 dB |
+| inputs from 5 mm slices | 24.66 dB | 26.82 dB |
+
+## Training
+
+The training recipe:
+
+- 3D Haar wavelet basis, with the low-pass band divided by 3;
+- five-level MONAI `DiffusionModelUNet` with channels (64, 128, 128, 256, 256), three residual blocks per
+  level and one self-attention block in the bottleneck;
+- mean squared error on the displacement at the source endpoint;
+- AdamW with learning-rate warmup and cosine decay, gradient clipping and bf16 mixed precision;
+- EMA decay 0.9999 and random flips;
+- checkpoint selection on 12 held-out cases.
+
+The BraTS model was fine-tuned from a network of the same architecture trained along the full bridge path,
+which is not released. The IXI model was trained from random initialisation. The configs and subject lists
+are in the code repository.
+
+## Intended use and limitations
+
+These are research models, not medical devices, and are not intended for clinical decisions. A synthesised
+T1c is a conditional mean: it cannot recover contrast enhancement that the acquired contrasts do not reveal,
+and it may show enhancement that is not there. The models assume co-registered inputs on the BraTS grid.
+Performance drops on misregistered or thick-slice inputs, which `primed_brats_robust` partly addresses. The
+models were trained on BraTS and IXI data only.
+
+## License
+
+CC BY-NC 4.0, for non-commercial use. The models are trained on
+[BraTS 2023](https://www.synapse.org/Synapse:syn51156910/wiki/621282) and
+[IXI](https://brain-development.org/ixi-dataset/) data; please respect their terms of use.

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