Add FORCE-Interior and baseline checkpoints

README.md

@@ -0,0 +1,110 @@
+---
+license: cc-by-nc-4.0
+library_name: pytorch
+pipeline_tag: image-to-image
+tags:
+- computed-tomography
+- interior-tomography
+- ct-reconstruction
+- pfgm++
+- diffusion-model
+- medical-imaging
+---
+
+# FORCE-Interior checkpoints
+
+Pretrained weights for **FORCE-Interior: Measurement-Consistent Adaptation of a
+Poisson-Flow Generative Prior for Interior CT**, together with the learned baselines
+it is compared against.
+
+Code: <https://github.com/Peteroos/FORCE-Interior>
+
+## Files
+
+| Path | Size | Model | Used by |
+|---|---|---|---|
+| `checkpoints/model_028.pth` | 2.3 GB | PFGM++ prior of FORCE-Interior | `run_force.py` |
+| `checkpoints/roi_ct_cnn/net-epoch-200.pth` | 62 MB | ROI-CT-CNN U-Net | `run_roicnn.py` |
+| `checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/` | 455 MB | DDS pixel-diffusion prior (third-party, mirrored) | `run_dds.py` |
+| `checkpoints/drunet/drunet_mayo.pth` | 131 MB | DRUNet denoiser for the PnP-ADMM / PnP-HQS baselines | PnP baselines |
+
+## Download
+
+This repository mirrors the `checkpoints/` layout that `config.py` expects. Download
+it into the root of the code repository. Keep `--include`; without it this model
+card overwrites the code repository's `README.md`.
+
+```bash
+git clone https://github.com/Peteroos/FORCE-Interior.git
+cd FORCE-Interior
+hf download Peteroos/FORCE-Interior --include "checkpoints/*" --local-dir .
+```
+
+## Model details
+
+### PFGM++ prior — `checkpoints/model_028.pth`
+
+- Unconditional PFGM++ prior on 512 × 512 full-dose CT slices from 8 patients of the
+  2016 AAPM/Mayo Low Dose CT Grand Challenge: L067, L096, L109, L143, L192, L286,
+  L291, L310. The test patients L333 and L506 were not used for training.
+- Pixel domain: HU clipped to [-1024, 3071] and mapped linearly to [-1, 1].
+- Architecture: the U-Net in `model/` with `num_res_blocks=3`,
+  `channel_mult=1,2,4,8,16` (290 M parameters).
+- Contents: `{"model": state_dict, "ema": [tensors in model.parameters() order]}`.
+  Sample with the **EMA** weights; `load_checkpoint()` in `run_force.py` does this.
+
+### ROI-CT-CNN — `checkpoints/roi_ct_cnn/net-epoch-200.pth`
+
+- U-Net post-processing of the truncated FBP (Han & Ye, *Med. Phys.* 2019),
+  trained by us on full-dose Mayo CT at an ROI radius of 128 px for 200 epochs.
+- Network: `baselines/roi_ct_cnn/unet.py` (`net_type="type1"`, 64 base channels);
+  attenuation is scaled so that water = 1.0.
+- Contents: `{"state_dict", "opt", "epoch", "net_type"}`.
+- The training/validation patient split of this checkpoint was not recorded.
+
+### DDS prior — `checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/`
+
+- Unmodified copy of
+  [`jiayangshi/lodochallenge_pixel_diffusion`](https://huggingface.co/jiayangshi/lodochallenge_pixel_diffusion)
+  (revision `b7e291c2`, sha256 of the U-Net weights verified), released under the MIT
+  license with [DM4CT](https://github.com/DM4CT/DM4CT) (ICLR 2026). It is mirrored
+  here only so that all baselines can be fetched from one place; the upstream model
+  card is kept in the folder. Please cite DM4CT when using it.
+- According to the DM4CT split, L333 is in this prior's training set and L506 in
+  its test set.
+- Intensity normalisation used by our adapter: `baselines/dds/dm4ct_norm.json`.
+
+### DRUNet — `checkpoints/drunet/drunet_mayo.pth`
+
+- DRUNet (Zhang et al., DPIR, *TPAMI* 2021) with a noise-level map as second input;
+  channels (64, 128, 256, 512), 4 residual blocks per level, 32.6 M parameters.
+- Trained on 1369 full-dose slices from 6 patients (L109, L143, L192, L286, L291,
+  L310). The validation patients (L067, L096) and test patients (L333, L506) were
+  excluded.
+- Same [-1, 1] pixel domain as the PFGM++ prior; additive Gaussian noise with
+  σ ~ U[0, 0.08] (0–164 HU); 30 000 iterations.
+- Contents: `{"state_dict", "iters", "patients", "sigma_max"}`.
+
+## Training data
+
+All models were trained on the 2016 AAPM/Mayo Low Dose CT Grand Challenge data.
+No CT images or data derived from them are distributed in this repository.
+
+## License
+
+The PFGM++ prior, ROI-CT-CNN and DRUNet weights are released under
+[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). The mirrored DDS prior in
+`checkpoints/dds_pretrained/` keeps its upstream MIT license.
+
+## Citation
+
+```bibtex
+@article{chen2026forceinterior,
+  title  = {{FORCE-Interior}: Measurement-Consistent Adaptation of a Poisson-Flow
+            Generative Prior for Interior {CT}},
+  author = {Chen, Kang and Xia, Wenjun and Wang, Jianxu and Nafee, Mahmud Wasif
+            and Wang, Ge},
+  note   = {Manuscript under review},
+  year   = {2026}
+}
+```

checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/README.md

@@ -0,0 +1,90 @@
+---
+library_name: diffusers
+license: mit
+pipeline_tag: image-to-image
+tags:
+- computed-tomography
+- ct-reconstruction
+- diffusion-model
+- inverse-problems
+- dm4ct
+- sparse-view-ct
+---
+
+# Pixel Diffusion UNet – LoDoChallenge (DM4CT)
+
+This repository contains the pretrained **pixel-space diffusion UNet** presented in the paper [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589) (ICLR 2026).
+
+- **Project Page:** [https://dm4ct.github.io/DM4CT/](https://dm4ct.github.io/DM4CT/)
+- **Codebase:** [https://github.com/DM4CT/DM4CT](https://github.com/DM4CT/DM4CT)
+- **Paper:** [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589)
+
+---
+
+## 🔬 Model Overview
+
+This model learns a **prior over CT reconstruction images** using a denoising diffusion probabilistic model (DDPM). It operates directly in **pixel space** (not latent space).
+
+- **Architecture**: 2D UNet (Diffusers `UNet2DModel`)
+- **Input resolution**: 512 × 512
+- **Channels**: 1 (grayscale CT slice)
+- **Training objective**: ε-prediction (standard DDPM formulation)
+- **Noise schedule**: Linear beta schedule
+- **Training dataset**: Low Dose Grand Challenge (LoDoChallenge)
+- **Intensity normalization**: Rescaled to (-1, 1)
+
+This model is intended to be combined with data-consistency correction for CT reconstruction tasks as detailed in the DM4CT benchmark.
+
+---
+
+## 📊 Dataset: Low Dose Grand Challenge
+
+Source: [AAPM Low Dose CT Grand Challenge](https://www.aapm.org/grandchallenge/lowdosect/)
+
+Preprocessing steps:
+- Train/test split.
+- Rescale reconstructed slices to (-1, 1).
+- No geometry information is embedded in the model.
+
+The model learns an unconditional image prior over medical CT slices.
+
+---
+
+## 🧠 Training Details
+
+- **Optimizer**: AdamW
+- **Learning rate**: 1e-4
+- **Hardware**: NVIDIA A100 GPU
+- **Training script**: [train_pixel.py](https://github.com/DM4CT/DM4CT/blob/main/train_pixel.py)
+
+---
+
+## 🚀 Usage
+
+You can use this model with the `diffusers` library:
+
+```python
+from diffusers import DDPMPipeline
+
+# Load the pretrained pipeline
+pipeline = DDPMPipeline.from_pretrained("jiayangshi/lodochallenge_pixel_diffusion")
+
+# Generate a sample (unconditional CT slice prior)
+image = pipeline().images[0]
+image.save("generated_ct_slice.png")
+```
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{
+  shi2026dmct,
+  title={{DM}4{CT}: Benchmarking Diffusion Models for Computed Tomography Reconstruction},
+  author={Shi, Jiayang and Pelt, Dani{\"e}l M and Batenburg, K Joost},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=YE5scJekg5}
+}
+```

checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/model_index.json

@@ -0,0 +1,12 @@
+{
+  "_class_name": "DDPMPipeline",
+  "_diffusers_version": "0.32.2",
+  "scheduler": [
+    "diffusers",
+    "DDPMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DModel"
+  ]
+}

checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/scheduler/scheduler_config.json

@@ -0,0 +1,19 @@
+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.32.2",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": true,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "steps_offset": 0,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}

checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/unet/config.json

@@ -0,0 +1,53 @@
+{
+  "_class_name": "UNet2DModel",
+  "_diffusers_version": "0.32.2",
+  "_name_or_path": "trained/lodochallenge/ddpm/unet",
+  "act_fn": "silu",
+  "add_attention": true,
+  "attention_head_dim": 8,
+  "attn_norm_num_groups": null,
+  "block_out_channels": [
+    128,
+    128,
+    256,
+    256,
+    512,
+    512
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "AttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "downsample_type": "conv",
+  "dropout": 0.0,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 1,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "num_train_timesteps": null,
+  "out_channels": 1,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 512,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "up_block_types": [
+    "UpBlock2D",
+    "AttnUpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ],
+  "upsample_type": "conv"
+}

checkpoints/dds_pretrained/lodochallenge_pixel_diffusion/unet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12a59a05edcebd9121b522345d6d82cbf2135d8e3523e2ebc52937078e9e7e53
+size 454722660

checkpoints/drunet/drunet_mayo.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7e837c179cccedba4e3b2bd757a26432e09ea46fc91a30796721e359812d53f
+size 130580966

checkpoints/model_028.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e5aa073b5f47ed7272fbaa068997ce54bdc76032975e752c2b56831e40cd82d
+size 2322797498

checkpoints/roi_ct_cnn/net-epoch-200.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09e1d39e0dbd5536eee569fa7d5eae57b65255edd1bfdfdbd94213efa8cbc7ff
+size 61694514